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The usage of psychological networks that conceptualize psychological behavior as a complex interplay of psychological and other components has gained increasing popularity in various fields of psychology. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online., Comment: Accepted for publication in Behavior Research Methods

Since the first patient was detected in India in late February 2020, the SARS-CoV-II virus is playing havoc on India. After the first wave, India is now riding the second wave. As was in the case of European countries like Italy and the UK, the second wave is more contagious and at the time of writing this paper, the per day infection is as high as 400,000. The alarming thing is it is not uncommon that people are getting infected multiple times. On the other hand, mass vaccination has started step by step. There is also a growing danger of potential third wave is unavoidable, which can even infect kids and minors. In this situation, an estimation of the dynamics of SARS-CoV-II is necessary to combat the pandemic. We have used a modified SEIRD model that includes vaccination and repeat infection as well. We have studied India and 8 Indian states with varying SARS-CoV-II infections. We have shown that the COVID-19 wave will be repeated from time to time, but the intensity will slow down with time. In the most possible situation, our calculation shows COVID-19 will remain endemic for the foreseeable future unless we can increase our vaccination rate manifold.

Research on heat waves (periods of excessively hot weather, which may be accompanied by high humidity) is a newly emerging research topic within the field of climate change research with high relevance for the whole of society. In this study, we analyzed the rapidly growing scientific literature dealing with heat waves. No summarizing overview has been published on this literature hitherto. We developed a suitable search query to retrieve the relevant literature covered by the Web of Science (WoS) as complete as possible and to exclude irrelevant literature (n = 8,011 papers). The time-evolution of the publications shows that research dealing with heat waves is a highly dynamic research topic, doubling within about 5 years. An analysis of the thematic content reveals the most severe heat wave events within the recent decades (1995 and 2003), the cities and countries/regions affected (United States, Europe, and Australia), and the ecological and medical impacts (drought, urban heat islands, excess hospital admissions, and mortality). Risk estimation and future strategies for adaptation to hot weather are major political issues. We identified 104 citation classics which include fundamental early works of research on heat waves and more recent works (which are characterized by a relatively strong connection to climate change)., 40 pages, 2 tables, and 9 figures

The currently ongoing COVID-19 pandemic confronts governments and their health systems with great challenges for disease management. Epidemiological models play a crucial role, thereby assisting policymakers to predict the future course of infections and hospitalizations. One difficulty with current models is the existence of exogenous and unmeasurable variables and their significant effect on the infection dynamics. In this paper, we show how a method from nonlinear control theory can complement common compartmental epidemiological models. As a result, one can estimate and predict these exogenous variables requiring the reported infection cases as the only data source. The method allows to investigate how the estimates of exogenous variables are influenced by non-pharmaceutical interventions and how imminent epidemic waves could already be predicted at an early stage. In this way, the concept can serve as an “epidemometer” and guide the optimal timing of interventions. Analyses of the COVID-19 epidemic in various countries demonstrate the feasibility and potential of the proposed approach. The generic character of the method allows for straightforward extension to different epidemiological models.

We investigate the problem of mathematical modeling of new corona virus (Covid19) in Poland and tries to predict the upcoming wave. A Gaussian mixture model is proposed to characterize the COVID-19 disease and to predict a new / future wave of COVID-19. This prediction is very much needed to prepare for medical setup and continue with the upcoming program. Specifically, data related to the new confirmed cases of COVID-19 per day are considered, and then we attempt to predict the data and statistical activity. A close match between actual data and analytical data by using the Gaussian mixture model shows that it is a suitable model to present new cases of COVID-19. In addition, it is thought that there are N waves of COVID-19 and that information for each future wave is also present in current and previous waves as well. Using this concept, predictions of a future wave can be made.

Leading indicators are important variables in business cycle forecasting. We use wavelet analysis to investigate the lead-lag stability of German leading indicators in time-frequency space. This method permits a time-varying relation of the leading indicators to the reference cycle allowing simultaneously to focus on lead-lag stability at the specific business cycle frequencies. In this way we analyze an index of new orders, a survey-based index of business expectations, an index of stock market returns and the interest rate term spread. We confirm that most of these indicators are indeed leading the reference cycle most of the time, but the number of months leading varies considerably over time and is associated with a great deal of estimation uncertainty.

We study an alternative approach to determine the final league table in football competitions with a premature ending. For several countries, a premature ending of the 2019/2020 football season has occurred due to the COVID-19 pandemic. We propose a model-based method as a possible alternative to the use of the incomplete standings to determine the final table. This method measures the performance of the teams in the matches of the season that have been played and predicts the remaining non-played matches through a paired-comparison model. The main advantage of the method compared to the incomplete standings is that it takes account of the bias in the performance measure due to the schedule of the matches in a season. Therefore, the resulting ranking of the teams based on our proposed method can be regarded as more fair in this respect. A forecasting study based on historical data of seven of the main European competitions is used to validate the method. The empirical results suggest that the model-based approach produces more accurate predictions of the true final standings than those based on the incomplete standings.

Non-invasive estimation of the pressure gradient in cardiovascular stenosis has much clinical importance in assisting the diagnosis and treatment of stenotic diseases. In this research, a systematic comparison is conducted to investigate the accuracy of a group of stenosis models against the MRI- and catheter-measured patient data under the aortic coarctation condition. Eight analytical stenosis models, including six from the literature and two proposed in this study, are investigated to examine their prediction accuracy against the clinical data. The two improved models proposed in this study consider comprehensively the Poiseuille loss, the Bernoulli loss in its exact form, and the entrance effect, of the blood flow. Comparison of the results shows that one of the proposed models demonstrates a cycle-averaged mean prediction error of −0.15 ± 3.03 mmHg, a peak-to-peak prediction error of −1.8 ± 6.89 mmHg, which is the best among the models studied., This research was funded by the European Community’s Seventh Framework Programme (FP7/2007–2013) under the grant agreement number 224495 (euHeart project).

This paper presents empirical results on coronavirus infection and fatality rates from cross-country regressions for OECD economies and a sample of middle- and high-income countries. We include environmental, economic, medical, and policy variables in our analysis to explain the number of corona cases and deaths per million. We find a significant positive impact of local air pollution on infection rates in the whole sample and on fatality rates for OECD countries. Obesity rates have a positive effect on cases and deaths across the different estimation equations. The strategy of aiming to achieve herd immunity has a significant positive effect on infections as well as on death rates. The first affected countries have significantly higher mortality rates, revealing the lack of experience and medical capacity to deal with the pandemic in an initial phase. Postponing – and fighting - the pandemic could save lives in many countries and generate considerable economic benefits. Other medical and policy variables discussed in the public sphere do not show a significant impact in the regression analysis. Our results suggest that improving air quality and fighting obesity helps reduce the negative effects of a coronavirus pandemic significantly. Policy options for fighting a second epidemic wave should take into account the results from this study in order to optimize global epidemic policy., International Economics and Economic Policy, 17 (4), ISSN:1612-4804, ISSN:1612-4812

Gambling is a popular leisure activity but also a significant public health issue in Australia. The severity of gambling in Australia is characterised by unprecedented gambling expenditures and losses representing the highest gambling rates in the world, which has led to renewed interests in understanding the factors influencing gambling behaviours. We contribute to the debate on the determinants of gambling by providing the first study that examines the impact of ethnic diversity on gambling. Using data from the Household, Income and Labour Dynamics in Australia survey, we find that ethnic diversity is positively associated with gambling. This result is robust to alternative estimation approaches, alternative ways of measuring ethnic diversity and irrespective of whether gambling is measured using the Problem Gambling Severity Index, gambling expenditures or number of gambling activities. Our results also suggest stronger effects of ethnic diversity for problem gamblers compared to gamblers in other risk categories. These results support the need for policies that promote social capital in diverse communities to reduce the risks of social isolation, which is an important motivator of gambling behaviour.

Only limited data are available in France on the incidence and health expenditure of type 2 diabetes. The objective of this study, based on national health insurance administrative database, is to describe the expenditure reimbursed to patients newly treated for type 2 diabetes and the proportion of expenditure attributable to diabetes. The study is conducted over a 6-year period from 2008, the year of incidence of treated diabetes, to 2014. Type 2 diabetic patients aged 45 years and older are identified on the basis of their drug consumption. To estimate expenditure attributable to diabetes, a matched control group is selected among more than 13 million beneficiaries over 44 years old not taking antidiabetic treatment. The expenditure attributable to diabetes is estimated by two methods: simple comparison of reimbursed health expenditure between both groups, and a difference-in-differences method including control variables. The cohort of incident type 2 diabetic patients comprises 170,013 patients in 2008. Mean global reimbursed expenditure is €4700 per patient in 2008 and €5500 in 2015. Expenditure attributable to diabetes, estimated by direct comparison with controls, is €1500 in the first year. We, thus, observe a decrease in the following year due to decreased hospitalisations, and then expenditure increase by an average of 7% per year to reach €1900 in the eighth year after the initiation of treatment.

Introduction The first wave of COVID-19 pandemic period has drastically changed people’s lives all over the world. To cope with the disruption, digital solutions have become more popular. However, the ability to adopt digitalised alternatives is different across socio-economic and socio-demographic groups. Objective This study investigates how individuals have changed their activity-travel patterns and internet usage during the first wave of the COVID-19 pandemic period, and which of these changes may be kept. Methods An empirical data collection was deployed through online forms. 781 responses from different countries (Italy, Sweden, India and others) have been collected, and a series of multivariate analyses was carried out. Two linear regression models are presented, related to the change of travel activities and internet usage, before and during the pandemic period. Furthermore, a binary regression model is used to examine the likelihood of the respondents to adopt and keep their behaviours beyond the pandemic period. Results The results show that the possibility to change the behaviour matter. External restrictions and personal characteristics are the driving factors of the reduction in ones' daily trips. However, the estimation results do not show a strong correlation between the countries' restriction policy and the respondents' likelihood to adopt the new and online-based behaviours for any of the activities after the restriction period. Conclusion The acceptance and long-term adoption of the online alternatives for activities are correlated with the respondents' personality and socio-demographic group, highlighting the importance of promoting alternatives as a part of longer-term behavioural and lifestyle changes.

We demonstrate a Bayesian method for the “real-time” characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted. The method also detected different disease dynamics when applied to specific regions of New Mexico.

The aim of this study was to evaluate observed and future inbreeding level in Polish Holstein-Friesian cattle population. In total, over 9.8 mln animals were used in the analysis coming from the pedigree of Polish Federation of Cattle Breeders and Dairy Farmers. Inbreeding level, as an average per birth year, was estimated with the method accounting for missing parent information with the assumption of year 1950 as the base year of the population. If an animal had no ancestral records, an average inbreeding level from its birth year was assigned. Twice the average inbreeding level served as relatedness of the animal to the population, which enabled estimation of inbreeding in its offspring. The future inbreeding of potential offspring was estimated as an average of animals (bulls and cows) available for mating in a certain year. It was observed that 30-50% of animals born between 1985 and 2015 had no relevant ancestral information, which is caused by a high number of new animals and/or entire farms entering the national milk recordings. For the year 2015, the observed inbreeding level was 3.30%, which was more than twice the inbreeding with the classical approach (without missing parent information) and higher by 0.4% than the future inbreeding. The average increase of inbreeding in years 2010-2015 was 0.10%, which is similar to other countries monitored by World Holstein-Friesian Federation. However, the values might be underestimated due to low pedigree completeness. The estimates of future inbreeding suggested that observed inbreeding could be even lower and also increase slower, which indicates a constant need to monitor rate of increase in inbreeding over time. The most important aspect of presented results is the necessity to advise individual farmers to keep precise recordings of the matings on their farm in order to improve the pedigree completeness of Polish Holstein-Friesian and to use suitable mating programs to avoid too rapid growth of inbreeding.

A key feature of this project is the estimation of broad industry sectors for each carcinogen, occupational circumstance and cancer site. This information is critical to prioritise interventions that aim to reduce workplace-related cancer. This paper presents results for the current burden of occupational cancer by industry sector on the basis of attributable cancer registrations for 2004, for all industries for which there are at least 10 attributable registrations.

Respondent-driven sampling (RDS) has become increasingly popular for sampling hidden populations, including injecting drug users (IDU). However, RDS data are unique and require specialized analysis techniques, many of which remain underdeveloped. RDS sample size estimation requires knowing design effect (DE), which can only be calculated post hoc. Few studies have analyzed RDS DE using real world empirical data. We analyze estimated DE from 43 samples of IDU collected using a standardized protocol. We find the previous recommendation that sample size be at least doubled, consistent with DE = 2, underestimates true DE and recommend researchers use DE = 4 as an alternate estimate when calculating sample size. A formula for calculating sample size for RDS studies among IDU is presented. Researchers faced with limited resources may wish to accept slightly higher standard errors to keep sample size requirements low. Our results highlight dangers of ignoring sampling design in analysis.

Many health technology assessment committees have an explicit or implicit reference value (often referred to as a ‘threshold’) below which new health technologies or interventions are considered value for money. The basis for these reference values is unclear but one argument is that it should be based on the health opportunity costs of funding decisions. Empirical estimates of the marginal cost per unit of health produced by a healthcare system have been proposed to capture the health opportunity costs of new funding decisions. Based on a systematic search, we identified eight studies that have sought to estimate a reference value through empirical estimation of the marginal cost per unit of health produced by a healthcare system for England, Spain, Australia, The Netherlands, Sweden, South Africa and China. We review these eight studies to provide an overview of the key methodological approaches taken to estimate the marginal cost per unit of health produced by the healthcare system with the aim to help inform future estimates for additional countries. The lead author for each of these papers was invited to contribute to the current paper to ensure all the key methodological issues encountered were appropriately captured. These included consideration of the key variables required and their measurement, accounting for endogeneity of spending to health outcomes, the inclusion of lagged spending, discounting and future costs, the use of analytical weights, level of disease aggregation, expected duration of health gains, and modelling approaches to estimating mortality and morbidity effects of health spending. Subsequent research estimates for additional countries should (1) carefully consider the specific context and data available, (2) clearly and transparently report the assumptions made and include stakeholder perspectives on their appropriateness and acceptability, and (3) assess the sensitivity of the preferred central estimate to these assumptions. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-021-01087-6.

Automated vehicles (AVs) are expected to operate on public roads, together with non-automated vehicles and other road users such as pedestrians or bicycles. Recent ethical reports and guidelines raise worries that AVs will introduce injustice or reinforce existing social inequalities in road traffic. One major injustice concern in today’s traffic is that different types of road users are exposed differently to risks of corporal harm. In the first part of the paper, we discuss the responsibility of AV developers to address existing injustice concerns regarding risk exposure as well as approaches on how to fulfill the responsibility for a fairer distribution of risk. In contrast to popular approaches on the ethics of risk distribution in unavoidable accident cases, we focus on low and moderate risk situations, referred to as routine driving. For routine driving, the obligation to distribute risks fairly must be discussed in the context of risk-taking and risk-acceptance, balancing safety objectives of occupants and other road users with driving utility. In the second part of the paper, we present a typical architecture for decentralized automated driving which contains a dedicated module for real-time risk estimation and management. We examine how risk estimation modules can be adjusted and parameterized to redress some inequalities.

This paper explores the advantages of international trade and foreign direct investment (FDI), which can be the main factors of transferring technology to economies. More specifically, it seeks the importance of international trade and FDI as the main channels of technology transfer between countries either in a region or at the international level. In this context, relevant spillovers arising from trade and FDI play important roles in achieving economic growth, capital accumulation and economic well-being, providing a path for sustainable development. The main goal of this paper is thus to investigate the possible implementation of regional economic through cooperation in trade and investment in a region such as the Economic Cooperation Organization (ECO). Accordingly, the country members can benefit from spillover effects on their economic growth, as a major factor to appoint sustainable development. The relevant methodology relies on estimation of a panel economic growth model in which we have used the obtained empirical results to assess the effects of trade and FDI spillovers on economic growth over the period 1995–2018. The empirical results have indicated significantly positive effects of the spillovers on economic growth of the ECO country members, as the case study, implying sustainable growth in the long run.

This paper updates and extends the systematic review and meta-analysis of Wardman and Batley (Transportation 41:1041–1069, 2014), which hitherto was the most comprehensive study of the impacts of punctuality on passenger rail demand in the literature. Whereas the 2014 paper covered 51 elasticities from 6 studies in Great Britain published between 2003 and 2011, this updated paper adds 11 subsequent British studies yielding a further 201 observations. The meta-model recovers a range of significant effects, relating to whether the elasticity was short versus long run, flow type and distance, season versus nonseason tickets, the relevant measure of lateness, and whether the purpose of the study was specifically the estimation of late time elasticities. Allowance was also made for study quality-related issues. The data indicated that, despite dynamic models being commonplace, there is some uncertainty as to how long the long run is. Alongside the meta-model, the paper also reports new econometric evidence that addresses some gaps in existing evidence and knowledge, especially in relation to functional form and non-linearity of effects. Findings from both strands of analysis would seem to suggest that rail industry guidance has tended to overstate the demand impacts of punctuality.

This paper examines the learning-by-exporting effect in Chinese manufacturing firms from 2005 to 2007. The traditional view is that exporting can lead to increased productivity by facilitating access to the global market and, thus, information and cutting-edge technologies. This process has been explained by the learning-by-exporting theory, which is supported by mixed empirical evidence. A semiparametric estimation method was used to measure firm-level productivity and examine the contingent impact of exports on productivity. On the one hand, the exporting firms exhibited significantly higher productivity and faster growth than the non-exporting firms. On the other hand, the effect of exporting on productivity was dependent upon firms’ innovation behavior. Therefore, the learning-by-exporting effect is contingent on a firm’s innovation capabilities. Only sufficiently innovative firms could actually experience faster growth through exports and innovation. For non-innovative firms, exporting could even result in decreased productivity. This paper successfully reconciles the mixed findings from the existing literature and explains why both positive and negative evidence can simultaneously and reasonably exist under learning-by-exporting theory.

Existing research on funding disparities between historically black colleges and universities (HBCUs) and non-HBCUs primarily focuses on government funding and pays little attention to foundation giving. This paper helps to fill this gap by examining the effects of funding by the Charles Koch Foundation—a major funder of economics research in the USA—on the economics research productivity at HBCUs. Using data from the foundation’s tax forms, the Integrated Postsecondary Education Data System (IPEDS), and Scopus, this paper examines factors that explain the foundation’s grant results and how the foundation’s money affects economics research productivity at HBCUs. The paper estimates the effects of Koch funding on three different measures of HBCU productivity: number of publications, citations, and publications in top economics journals. We report ordinary least squares, two-step generalized linear model, and Poisson results that show little or no impact from (a) recipiency of a Koch grant, (b) the total amount of grant dollars, or (c) the average amount of grant dollars on research productivity. We also obtain event study and difference-in-differences estimates of the effects of Koch grants to HBCUs on publications and publications per faculty member and again find no statistically significant impacts. The evidence obtained—consistent across different model specifications and estimation techniques—does not support the hypothesis that Koch’s funding to HBCUs is primarily geared towards enhancing the research productivity of these colleges and universities. The paper discusses alternative explanations for the Charles Koch Foundation’s targeting strategy and its involvement with particular HBCUs.

This paper provides the humanitarian community with an automated tool that can detect a disaster using tweets posted on Twitter, alongside a portal to identify local and regional Non-Governmental Organisations (NGOs) that are best-positioned to provide support to people adversely affected by a disaster. The proposed disaster detection tool uses a linear Support Vector Classifier (SVC) to detect man-made and natural disasters, and a density-based spatial clustering of applications with noise (DBSCAN) algorithm to accurately estimate a disaster’s geographic location. This paper provides two original contributions. The first is combining the automated disaster detection tool with the prototype portal for NGO identification. This unique combination could help reduce the time taken to raise awareness of the disaster detected, improve the coordination of aid, increase the amount of aid delivered as a percentage of initial donations and improve aid effectiveness. The second contribution is a general framework that categorises the different approaches that can be adopted for disaster detection. Furthermore, this paper uses responses obtained from an on-the-ground survey with NGOs in the disaster-hit region of Uttar Pradesh, India, to provide actionable insights into how the portal can be developed further.

In this paper, we highlight some recent developments of a new route to evaluate macroeconomic policy effects, which are investigated under the framework with potential outcomes. First, this paper begins with a brief introduction of the basic model setup in modern econometric analysis of program evaluation. Secondly, primary attention goes to the focus on causal effect estimation of macroeconomic policy with single time series data together with some extensions to multiple time series data. Furthermore, we examine the connection of this new approach to traditional macroeconomic models for policy analysis and evaluation. Finally, we conclude by addressing some possible future research directions in statistics and econometrics.

The paper estimates banks’ total factor efficiency (TFE) as well as TFE of each production factor by incorporating banks’ overall risk endogenously into bank’s production process as undesirable by-product in a Global-SMB Model. Our results show that, compared with a model incorporated with banks’ overall risk, a model considering only on-balance-sheet risk may over-estimate the integrated TFE (TFIE) and under-estimate TFE volatility. Significant heterogeneities of bank TFIE and TFE of each production factor exist among banks of different types and regions, as a result of still prominent unbalanced development of Chinese commercial banks. Based on the estimated TFIE, the paper further investigates the determinants of bank efficiency, and finds that shadow banking, bank size, NPL ratio, loan to deposit ratio, fiscal surplus to GDP ratio and banking sector concentration are significant determinants of bank efficiency. Besides, a model with risk-weighted assets as undesirable outputs can better capture the impact of shadow banking involvement.

Background The determination of the shooting distance using gunshot residue (GSR) analysis is crucial in the investigation and reconstruction of firearm-related crimes. However, the conventional chemographic method for GSR analysis is destructive and has limited sensitivity and selectivity. While the spectroscopic method has potential in GSR analysis for crime investigation, there is a current lack of consistency in the spectroscopic results obtained for shooting distance estimation via GSR analysis. Addressing such limitations will enhance the forensic capabilities of law enforcement and provide an added advantage to crime laboratories during an investigation. It will also reinforce the use of such spectroscopic data in a criminal investigation. Main text We obtained all peer-reviewed articles relevant to shooting distance estimation from searching Scopus, Web of Science, PubMed, and Google Scholar databases. We specifically searched the databases using the keywords “shooting distance,” “range of fire,” “gunshot residue,” “firearm discharge residue,” and “firearm-related crime” and obtained 3811 records. We further filtered these records using a combination of two basic keywords “gunshot residue” and “shooting distance estimations” yielding 108 papers. Following a careful evaluation of the titles, abstracts, and full texts, 40 original peer-reviewed articles on shooting distance estimation via GSR analysis were included in the study. The forgoing included additional sources (n = 5) we obtained from looking through the reference lists of the forensic articles we found. Short conclusion This paper discusses the current scope of research concerning the chemographic and spectroscopic analysis of GSR for shooting distance estimation. It also examines the challenges of these techniques and provides recommendations for future research.

In statistical survey, except sampling error, the error caused by other reasons is called non-sampling error. Non-sampling errors may affect survey results. This paper mainly studies the emergence of non-sampling error in the system, the corresponding history and culture and related applications, seeks the causes of the error and explores the solutions, and designs a method that can be used to measure. By using this analysis method, the percentage of non-sampling error in the total error can be adjusted through statistics. In addition, the adaptability and path of this measurement method, as well as the adjustment method for the measurement error and estimation problems found in the multi-order model are also studied. As we all know, the subtropical northeast Pacific plays an extremely important role in the whole process of transmission to the tropics. Through the investigation of the physical and mental health, interpersonal relationship, and other problems of the students in a medical college, this paper analyzes the intervention effect of this treatment method by using a variety of treatment methods such as music and song performance organized by the masses, and probes into whether this intervention method can promote the improvement of college students’ mental health level and test the effectiveness of the treatment effect. It provides an important reference for the development of physical and mental health education of college students. This paper discusses the effect of psychological intervention in music education by simulating sea surface temperature with non-sampling error.

In order to solve the problem of the main parameters setting of traditional RBF neural network learning algorithm and the lack of local information based on space, this paper proposes a particle swarm hybrid optimization RBF neural network prediction algorithm based on genetic operation, which solves the early problems of PSO on the basis of the cross and mutation fusion of PSO and GA advantages. At the same time, we analyze the limitations of using the mountain rainfall simulation process and taking the mountain rainfall estimation as the model input, and put forward the idea of using the radar estimation technology from the perspective of spatial rainfall, and carrying out the mountain rainfall evaluation and simulation according to the spatial rainfall. The application of flood forecasting focuses on the research of mountain rainfall estimation simulation method based on radar precipitation estimation technology, and it is also the combination of mountain rainfall simulation process. Finally, this paper evaluates and analyzes online English teaching, and the new network environment has become the carrier of College English learning. In the new network environment, students will form the awareness of reading, listening, and speaking English, and conduct conscious online English learning guidance and enhance their language ability to find their own understanding and perception of English. In this paper, we reveal the problems and shortcomings of the current college English education evaluation in China, put forward various new intelligence evaluation theories, and try to explore the practical functions of several education evaluation systems in education, so as to better use the means of education evaluation to improve the efficiency of College English teaching. According to the research of the RBF neural network, it is applied to rainfall estimation and teaching evaluation, which can effectively improve rainfall estimation and teaching evaluation.

In the past decades, global climate change has greatly changed the natural environment. Due to the lack of measurement data and technical means, there is no consensus on the structure and dynamic mechanism of the b-sea cycle. In this paper, based on ROMs model and grey estimation of parameters, a three-dimensional baroclinic model of thermodynamic coupling in b-ocean region under new background field is established by using high-quality data recently obtained. According to the simulation results and comparative experiments, the seasonal distribution characteristics of b-sea circulation and the water exchange form between b-sea and high seas are analyzed and explained. And many scientific problems of coastal circulation, such as education and mutation mechanism. However, good physical exercise is an important way to improve the physical quality of residents. As the basic requirement of residents’ physical exercise, the community sports environment affects and limits the residents’ physical exercise behavior. Environmental behavior studies show that the relationship between environment and behavior is not directly related to the individual’s response to environmental stimuli. The principle of “applicability” should be emphasized in the construction of sports environment in society. Therefore, the residents’ behavior evaluation of the school sports environment should become an important feedback information in the construction of community sports environment. The purpose of this paper is to analyze the correlation between residents’ sports environment evaluation and sports exercise behavior orientation, and to provide a theoretical basis for improving residents' sports exercise behavior and optimizing and perfecting the sports environment of the community.

In statistical survey, except sampling error, the error caused by other reasons is called non-sampling error. Non-sampling errors may affect survey results. This paper mainly studies the emergence of non-sampling error in the system, the corresponding history and culture and related applications, seeks the causes of the error and explores the solutions, and designs a method that can be used to measure. By using this analysis method, the percentage of non-sampling error in the total error can be adjusted through statistics. In addition, the adaptability and path of this measurement method, as well as the adjustment method for the measurement error and estimation problems found in the multi-order model are also studied. As we all know, the subtropical northeast Pacific plays an extremely important role in the whole process of transmission to the tropics. Through the investigation of the physical and mental health, interpersonal relationship, and other problems of the students in a medical college, this paper analyzes the intervention effect of this treatment method by using a variety of treatment methods such as music and song performance organized by the masses, and probes into whether this intervention method can promote the improvement of college students’ mental health level and test the effectiveness of the treatment effect. It provides an important reference for the development of physical and mental health education of college students. This paper discusses the effect of psychological intervention in music education by simulating sea surface temperature with non-sampling error.

This paper investigates the CO2 emissions–economic growth relationship in Kazakhstan for the period 1992–2013. Johansen, ARDLBT, DOLS, FMOLS, and CCR cointegration methods are used for robustness purpose. We start with the cubic functional form to rule out any misleading results that can be caused by misspecification. Although the estimation results suggest “U”-shaped relationship, the turning point of income is out of the period. It means that the impact of economic growth on CO2 is monotonically increasing in the long run indicating the Environmental Kuznets Curve (EKC) hypothesis does not hold for Kazakhstan. Moreover, we calculate that the income elasticity of CO2 is about unity. The paper concludes that the Kazakhstani policymakers should focus on less energy-intensive sectors as well as using more renewable energy in order to avoid higher pollution effects of economic growth. They may also set new policy regulations for CO2 reduction.

The relevant economics literature on the impact of R&D on patenting activity falls within two methodological areas of inquiry. The first area might be classified as a test of the Schumpeterian hypothesis. The second and lesser research area might be classified as an estimation of the knowledge production function relationship between R&D and patenting. This paper focuses on estimates of the R&D-to-patenting relationship for a random sample of small, entrepreneurial firms whose research projects were supported through the US Small Business Innovation Research (SBIR) program. Our paper contributes to the R&D-to-patenting literature in two ways. It examines empirically a unique set of small, entrepreneurial firms funded by the public sector, and it explores the effect of the gender and ethnicity of firm owners on the propensity of their firms to patent from funded research projects.

The simple answer to both questions in the title of this paper: No. We concentrate on three key aspects of the banking system's difficulties during the 2001–02 crisis. Two are related to bank behavior (increasing dollarization of the balance sheet and expanding exposure to the government), and the other is related to the degree by which banks were hurt by depositor preferences, specifically, the run on deposits during 2001. We find that there was substantial cross-bank variation, that is, not all banks behaved equally nor were hurt equally by the macroeconomic shocks they faced during the run-up to the crisis. Furthermore, using panel data estimation, we find that depositors were able to distinguish high-risk from low-risk banks, and that individual bank's exposure to currency and government default risk depended on fundamentals and other bank-specific characteristics. Finally, our results have implications for the existence of market discipline in periods of stress, and for banking regulation, which may have led banks to underestimate some of the risks they incurred. IMF Staff Papers (2007) 54, 621–662. doi:10.1057/palgrave.imfsp.9450022

While the wide-ranging impact of COVID-19 on incomes and livelihoods of people around the world will take some time to become known and understood, it is already clear that those who are in manual, mostly low-wage, occupations are among the worst-hit workers. This paper uses data from a sample survey of migrant workers to come up with useful parameters for estimating the potential losses from possible retrenchment of migrant workers due to the pandemic. The paper employs a simple estimation model using parameters derived from data collected from a KNOMAD-ILO survey of low-skilled migrant workers in the India-Saudi Arabia migration corridor, conducted during 2016-2017. An important finding is that the aggregate losses that low-skilled Indian workers in Saudi Arabia are likely to incur due to COVID-19-related retrenchment may be as high as 21% of their expected earnings. Adding recruitment costs can push up their losses to 36% of expected or potential earnings, while the aggregate remittances to their families could drop by USD 2 billion.

In this paper, we analyse the intergenerational transmission of economic success in Austria using the European Union Statistics on Income and Living Conditions 2011 dataset (EU-SILC 2011). Starting point of the investigation is a two-step estimation procedure, where we detect a significant positve intergenerational association between the economic situation of the parental household and educational attainments as well as gross hourly wages of their male and female descendants independently. Furthermore, we shed some light on the intergenerational social mobility black box by explaining the direct effect of the income situation of the parental generation on attainable wages of the child generation within the transmission system. It turns out that this effect is significantly underestimated when applying ordinary least squares regressions only, where the perceived socioeconomic status is taken exogenous. Finally, we apply instrumental variable quantile regressions to demonstrate that the direct intergenerational economic association between parents and their descendants is strongest for top earners. Alongside this proceeding, we introduce an alternative way to think about the impact of a bundle of additional intergenerational transmission channels like cognitive ability, noncognitive personal traits, and aspects of physical appearance on an empirical level. Overall, the findings of the paper, where special attention is paid to migrants, offer a better understanding on the intergenerational transmission of economic success mechanism and to what extent this process influences income persistence between generations.

Soil plays a vital role in the climate system. This paper performs decision tree regression to estimate soil moisture (SM) by considering different parameters that include air temperature, time, relative humidity, and soil temperature. Besides, this paper investigates the effects of the parameters of decision tree regression by utilizing the response surface. The obtained estimation results of two distinct soil areas, Field and Forest, indicate that two different soil areas have distinct estimation quality. Furthermore, numerical results of the training stage show that the estimation of SM for Field and Forest soil performing decision tree regression offers 0.0019 and 0.0025 mean absolute error (MAE), respectively. Moreover, numerical results show that the interaction of the parameters of the performed algorithm plays a vital role in the estimation stage of Field and Forest soils. WOS:000525759400001 2-s2.0-85083407257 PubMed: 32291529

Lost circulation costs are a significant expense in drilling oil and gas wells. Drilling anywhere in the Rumaila field, one the world’s largest oilfields, requires penetrating the Dammam formation, which is notorious for lost circulation issues and thus a great source of information on lost circulation events. This paper presents a new, more precise model to predict lost circulation volumes, equivalent circulation density (ECD), and rate of penetration (ROP) in the Dammam formation. A larger data set, more systematic statistical approach, and a machine-learning algorithm have produced statistical models that give a better prediction of the lost circulation volumes, ECD, and ROP than the previous models for events. This paper presents the new model, validates the key elements impacting lost circulation in the Dammam formation, and compares the predicted outcomes to those from the older model. The work previously presented by Al-Hameedi et al. ( http://www.onepetro.org , 2017a; http://www.AADE.org , 2017b) provided a platform for predicting the severity of lost circulation incidents in the Dammam formation. Using the new models, the predictions closely track actual field incidents of lost circulation. When new lost circulation events were compared with predictions from the old and new models, the new model presented a much tighter prediction of events. Three equations for optimizing operations were developed from these models focusing on the elements that have the highest degree of impact. The total flow area of the nozzles was determined to be a significant factor in the ROP model indicating that nozzle size should be chosen carefully to achieve optimal ROP. Good modeling of projected lost circulation events can assist in evaluating the effectiveness of new treatments for lost circulation. The Dammam formation is a significant source of lost circulation in a major oilfield and warrants evaluation of the effectiveness of lost circulation treatments. These techniques can be applied to other fields and formations to better understand the economic impact of lost circulation and evaluate the effectiveness of various lost circulation mitigation efforts.

Objectives of monetary policy have always been debated intensively. This debate has resurfaced during past few years in some countries including India where formulation of monetary policy has undergone significant change during the last two decades and is increasingly responding to changing macroeconomic environment. Against this backdrop, this paper models the monetary policy response function for India, for the period April 1996–July 2015. Using 91-day Treasury bill rate as the policy rate, the study finds that monetary policy in India has been responsive to inflation rate, output gap and exchange rate volatility (proxied by the rate of depreciation of INR/USD) during this period. This paper finds evidence in favour of substantial time varying behavior in the reaction function. The regime shift tests show that the transition is governed by inflation gap as well as exchange rate volatility (depreciation rate). Highly complex dynamics of interest rate restricted estimation of multiple models, but the models which are estimated show that the response function responds to inflation gap as well as exchange rate volatility. Another important finding of the study is high degree of inertia in the policy rates.

The main aim of this paper is to investigate the impact of patent applications, development level, employment level and degree of technological diversity on innovation efficiency. Innovation efficiency is derived by relating innovation inputs and innovation outputs. Expenditures in Research and Development and Human Capital stand for innovation inputs. Technological knowledge diffusion that comes from spatial and technological neighborhood stands for innovation output. We derive innovation efficiency using Data Envelopment Analysis for 192 European regions for a 12-year period (1995–2006). We also examine the impact of patents production, development and employment level and the level of technological diversity on innovation efficiency using Structural Equation Modeling. This paper contributes a method of innovation efficiency estimation in terms of regional knowledge spillovers and causal relationship of efficiency measurement criteria. The study reveals that the regions presenting high innovation activities through patents production have higher innovation efficiency. Additionally, our findings show that the regions characterized by high levels of employment achieve innovation sources exploitation efficiently. Moreover, we find that the level of regional development has both a direct and indirect effect on innovation efficiency. More accurately, transition and less developed regions in terms of per capita GDP present high levels of efficiency if they innovate in specific and limited technological fields. On the other hand, the more developed regions can achieve high innovation efficiency if they follow a more decentralized innovation policy.

This paper extends the literature on the estimation of expected utility and non-expected-utility preference functionals (and the consequent exploration of the superiority of non-expected-utility over expected utility preference functionals) to a comparison of two different ways (pairwise choice and complete ranking) of experimentally obtaining data on such preferences. What is revealed is that the magnitude of the subject error is clearly conditional on the elicitation method used and, rather alarmingly, that the preference functional apparently employed by the subject may also be conditional on the elicitation method. The Geneva Papers on Risk and Insurance Theory (1995) 20, 111–133. doi:10.1007/BF01098961

This paper introduces a proposed method for identifying the left and right Returns to Scales (RTS) and the overall RTS of frontier units based on the theoretical framework of data envelopment analysis (DEA). This paper first proposes an alternative definition for Left RTS (L-RTS) and Right RTS (R-RTS) of frontier units and then by focusing on this definition, provides two new DEA models to present a proposed method for identifying the L-RTS and R-RTS of frontier units. The major advantages of these two models include that they are feasible in all situations. Also, these models are capable to properly and accurately identify frontier units and their type of L-RTS and R-RTS, without any plurality in their judgment. The proposed method is compared -theoretically and experimentally- with other methods presented in the literature. Moreover, the theoretical correlation between the concepts of overall RTS and L&R-RTS of frontier units is analyzed. Then, this analysis is used to propose an alternative method for categorizing overall RTS of frontier units. Experimental application of this method shows that the results of evaluation of overall RTS of frontier units obtained by the proposed method is completely consistent with the results of the method presented by Banker and Thrall (Eur J Oper Res 62(1):74–84, 1992). The proposed methods in this paper can be easily used by the existing codes of DEA.

In this paper, a new access log analysis is proposed which estimates both active states and inactive states from observations simultaneously. I improved burst analysis and developed enthusiasm analysis to detect not only active states but also inactive states. Speaking concretely, I constructed a generative model that assumed observations, which meant event occurrence frequency in this paper, were generated under some Poisson distributions. The generative model based on Poisson distributions consists of some distributions including $$\lambda$$?s which are less than average frequency and more than average frequency. A cost function, which consists of log likelihood and state transition cost, is defined and enthusiasm levels are estimated from the observations minimizing the cost function. The proposed method was applied to query occurrence data in access logs and I confirmed the proposed method could find active states and inactive states from submission patterns and the results showed relationship between enthusiasm levels and real events.

Rising global temperatures are leading to an increase in the number of extreme events and losses ( http://www.epa.gov/climatechange/science/indicators/ ). Accurate estimation of these extreme losses with the intention of protecting themselves against them is critical to insurance companies. In a previous paper, Gulati et al. (2014) discussed probable maximum loss (PML) estimation for the Florida Public Hurricane Loss Model (FPHLM) using parametric and nonparametric methods. In this paper, we investigate the use of semi-parametric methods to do the same. Detailed analysis of the data shows that the annual losses from FPHLM do not tend to be very heavy tailed, and therefore, neither the popular Hill’s method nor the moment’s estimator work well. However, Pickand’s estimator with threshold around the 84th percentile provides a good fit for the extreme quantiles for the losses.

The Three-River Headwater Region (TRHR), known as the “Water Tower of China”, is an important ecological shelter for national security interests and regional sustainable development activities for many downstream regions in China and a number of Southeast Asian countries. The TRHR is a high-elevation, cold environment with a unique, but typical alpine vegetation system. Net primary productivity (NPP) is a key vegetation parameter and ecological indicator that can reflect both natural environmental changes and carbon budget levels. Given the unique geographical environment and strategic location of the TRHR, many scholars have estimated NPP of the TRHR by using different methods; however, these estimates vary greatly for a number of reasons. To date, there is no paper that has reviewed and assessed NPP estimation studies conducted in the TRHR. Therefore, in this paper, we (1) summarized the related methods and results of NPP estimation in the TRHR in a systematic review of previous research; (2) discussed the suitability of existing methods for estimating NPP in the TRHR and highlighted the most significant challenges; and (3) assessed the estimated NPP results. Finally, developmental directions of NPP estimation in the TRHR were prospected.

In medico-legal cases, most often the personal identity of the deceased is a mystery. The stature, sex and other parameters in such scenarios are ascertained using the physical evidence present at the crime scene. One of the key methods of ascertaining the sex and stature is by using the human bones. The method of achieving accuracy in estimation of stature from bones has been well established in past. There are several regression formulae for conducting such estimation. However, it must be kept in mind that these regression equations can vary depending upon the population and region. Thus, it is very necessary to study a particular population thoroughly before formulating regression equations for that specific population patch. In this paper, we have penned down the study of KORI POPULATION, who are native to Kanpur region of Uttar Pradesh state, in India. In this study, we have observed the statistics of 202 individuals (106 females and 96 males). In totality, eight bone dimensions including stature, total arm length, length of the middle finger, knee length, foot length, foot breadth, maximum head length and maximum head breadth have been recorded in this research paper. The regression formulae for females and males have been derived separately. Further, there are various parameters that have been compared to find which parameter provides the best results in terms of accuracy in stature estimation.

Rain attenuation becomes the prime reason for link failure at frequencies above 10 GHz. C band and Ku band are already exhausted for Indian region satellite communication and time has come to shift to next higher Ka-band. But the attenuation due to rain is very large for this band, especially for tropical country like India. This has led to the development of many rain attenuation prediction models. But to apply rainfall data in these models, fine rainfall data is required which is not available for most locations, and if available, are not for longer durations. In this paper, a novel method is described that can be used for rainfall attenuation prediction for India using coarse rainfall data, that is available from Indian Meteorological Department. Many models are available to predict the 1 min integration rainfall distribution around the world. But for Indian subcontinent, most models show large variations from actual rainfall attenuation. This paper presents estimation of rainfall attenuation for Ka-band for five different regions covering almost whole India using ITU-R model, Crane-Global model and Moupfouma model. These estimations are compared with actual measured results of previous works. The calculated rainfall rates suggest significant variances in the mean rainfall intensity or rate in mm/h. The analysis shows that the ITU-R model overestimates the rainfall intensity rates when empirical model is developed using data from Indian Meteorological Department. It is observed that ITU-R model is best suited for Indian region, but needs modifications to obtain accurate estimation for rain attenuation. Hence a new empirical model named Dafda–Maradia model for rain attenuation for India is proposed. This model is based on ITU-R model and is basically a modified ITU-R model. Here the prediction of rainfall attenuation is done from coarse rain data of 64 years (1951–2014). It is observed that there is a large decrease in the average rainfall intensity as compared to the ITU-R model.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

In this paper, estimation of entropy for Weibull distribution based on record values is considered. Maximum likelihood estimation and Bayes estimation for Shannon entropy and Renyi entropy have been considered based on record values. Bayes estimators are obtained using Markov Chain Monte Carlo method. A simulation study is performed to find the performance of the estimators developed in this paper. The inferential procedures developed in this paper have also been illustrated using real data.

Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method. © 2016, Springer-Verlag Berlin Heidelberg.

Numerous papers have documented a positive association between height and good physical health and also with good economic outcomes such as earnings. A smaller number have argued for an association with well-being. In this paper, the SHARE survey of over 50 year olds in Europe is used to analyse whether individuals’ height is associated with higher or lower levels of life-satisfaction using ordered probit estimation. In simple models there is a positive, concave relationship between height and life satisfaction. However it is shown that the results are quite sensitive to the inclusion of controls reflecting demographics and, in particular, human capital and health status. Where effects do exist, it is predominantly at low to medium levels of height. That is there is a penalty, in well-being terms, to being short but not necessarily a benefit to being tall. There is also evidence of heterogeneity across countries with the effects being best determined in France.