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In this paper, we investigate whether the use of accounting conservatism in India decreases IPO underpricing, which is of attention to stakeholders and supervisors. Furthermore, the study examines how asymmetry information affects the implication of accounting conservatism for IPOs. Based on a regression analysis of 527 firms that went public through IPOs of "A" shares listed on the national stock exchanges between 2000 and 2020, the paper also examines whether the relationship between conservatism and under-pricing is robust to alternative measures of accounting conservatism, mean regressions, sample exclusions, and endogenous treatment models. The research study finds that accounting conservatism is negatively associated with the degree of IPO under-pricing, and the association between accounting conservatism and IPO under-pricing is more perceptible when information asymmetry is high. The paper's originality should shed light on what drives IPO underpricing and how it could be affected by accounting conservatism in an Indian economy, and provide find evidence that legal origin, a factor linked to the practice of conservatism, influences the relationship between under-pricing and conservatism. [ABSTRACT FROM AUTHOR]

The article presents abstracts and titles of abstracts submitted for papers presented at the 1969 Annual Meetings of the American Statistical Association and Biometric Society in New York. Some of the abstracts are Financial and Non-Financial Factors Affecting Post-High School Plans, 1939-1965," by Walter Adams, "Experimental Designs and Mathematical Models," by Sidney Addelman, "Consistency of Linear Least Squares Estimate in a Regression Model with Lagged Variable," by M. Ashanullah, "The Use of Prior Information in Testing of New Drugs," by David M. Allen, "A Comparison of Probability Density Estimates," by Gary Anderson, "Stochastic Models of City Population Density," by Robert T. Amsden, "ldentification and Input Signal Synthesis Problems in Discrete-Time Stochastic Control Systems," by Masanao Akoi, "A Computer Language for the Analysis of Variance," by David J. Aemor, "A Generalized Mathematical Model for Biological Recovery with Applications in Radiation Biology," by George M. Angelton."

The permanent changes in society affect, among other, the environment. This is why environmental efficiency plays a very important role, being quantified in different forms. In our paper we have developed a composite index of ecological efficiency taking into account two dimensions, environmental pollution and resource consumption, each one being characterized by specific indicators. Thus, using this index, the aim was to evaluate and rank the level of greening of each country in Europe. Crises over time, including the health crisis caused by coronavirus, have focused to resilience, so we have highlighted whether it is significantly influenced by the ecological efficiency index. In addition, we analyzed whether ecological efficiency is related to investments in a country, financial, material and technological potential. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to identify the driving elements of the South African financial sector. While South Africa's financial sector appears robust, there exists a dearth of empirical research investigating the determinants of its development. Thus, this work assesses how three critical factors: natural resource abundance (NR), IT infrastructure, and government expenditure levels affect financial development (FD) in South Africa using annual data from 1971 to 2020. Preliminary findings show that the series are integrated, and they are cointegrated. Results from regression analysis suggest that the abundance of NR does not significantly contribute to financial development in South Africa. Conversely, advanced IT infrastructure, larger government size, and openness to trade are associated with a more developed financial sector. The implications of these findings are essential for policymakers and stakeholders in understanding the factors that drive financial development in South Africa. The study recommends that, among others, innovative approaches are needed to channel gains from natural resources effectively into the financial sector. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of classification of misaligned multivariate functional data. We propose to use a model-based approach for the joint registration and classification of such data. The observed functional inputs are modeled as a functional nonlinear mixed effects model containing a nonlinear functional fixed effect constructed upon warping functions to account for curve alignment, and a nonlinear functional random effects component to address the variability among subjects. The warping functions are also modeled to accommodate common effect within groups and the variability between subjects. Then, a functional logistic regression model defined upon the representation of the aligned curves and scalar inputs is used to account for curve classification. EM-based algorithms are developed to perform maximum likelihood inference of the proposed models. The identifiability of the registration model and the asymptotical properties of the proposed method are established. The performance of the proposed procedure is illustrated via simulation studies and an analysis of a hyoid bone movement data application. The statistical developments proposed in this paper were motivated by the hyoid bone movement study, the methodology is designed and presented generality and can be applied to numerous areas of scientific research. [ABSTRACT FROM AUTHOR]

Financial distress (FD) is a crucial issue that needs to be resolved in time; otherwise, it may adversely influence society's areas. Therefore, identifying the factors responsible for FD and how these factors affect FD is an exciting area of research. Given this, this paper proposes investigating the effect of competition on the bank's FD in India. This paper uses the data of 34 banks operating in India for the observation period (2016–2019). Three variants of Altman's Z-score are used for the assessment of FD. Lerner's index of the banks is developed for the competition measurement. A panel data analysis is further performed for regression analysis. The current study has an interesting outcome. The findings reveal that high market power or low competition lowers financial distress and adds more financial stability in banks in India. This paper provides fresh evidence on the connection between market power and FD of banks in India. Moreover, no other study has been observed to use all the three variants of Altman's z-score to provide robustness and reliability in results. Therefore, the study has a significant contribution and a novelty in its approach. [ABSTRACT FROM AUTHOR]

This paper presents a thermal error model for a lathe CNC machine using temperature on moving parts, which is usually a difficult measurement. Different working conditions are performed to obtain temperature and thermal displacement of the machine. A multi-linear regression model is applied for modelling relationship between temperatures and thermal errors. Data of two constant working conditions are implemented to find out the fitting function. Through analysis, it shows that thermal errors for X- and Z-axes can be estimated based on only four temperature points (T2, T3, T10 and T12 for the X-axis and T4, T6, T7 and T11 for the Z-axis). Results reveal that using movable part temperature data (T12), the average and maximum error in the predicted thermal displacement of the X-axis can be reduced about 1/3 and 2/5 compared to without using T12, while the movable temperature point on Z-Screw (T8) has a tiny effect on the average and maximum error of predicted thermal displacement of the Z-axis. Results herein can provide useful information to develop a thermal compensation for the CNC lathe machine. [ABSTRACT FROM AUTHOR]

As maritime activities increase globally, there is a greater dependency on technology in monitoring, control, and surveillance of vessel activity. One of the most prominent systems for monitoring vessel activity is the Automatic Identification System (AIS). An increase in both vessels fitted with AIS transponders and satellite and terrestrial AIS receivers has resulted in a significant increase in AIS messages received globally. This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics, of which a pertinent example is the improvement of vessel location predictions. In this paper, we propose a novel strategy for predicting future locations of vessels making use of historic AIS data. The proposed method uses a Linear Regression Model (LRM) and utilizes historic AIS movement data in the form of a-priori generated spatial maps of the course over ground (LRMAC). The LRMAC is an accurate low complexity first-order method that is easy to implement operationally and shows promising results in areas where there is a consistency in the directionality of historic vessel movement. In areas where the historic directionality of vessel movement is diverse, such as areas close to harbors and ports, the LRMAC defaults to the LRM. The proposed LRMAC method is compared to the Single-Point Neighbor Search (SPNS), which is also a first-order method and has a similar level of computational complexity, and for the use case of predicting tanker and cargo vessel trajectories up to 8 hours into the future, the LRMAC showed improved results both in terms of prediction accuracy and execution time. [ABSTRACT FROM AUTHOR]

This paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN models with the aim to enhance their predictive accuracy. Utilizing the Keras Tuner library, the most effective hyperparameters for the DNN models were identified. These models are then trained and evaluated using an experimental dataset of 1030 instances, encompassing nine quantitative variables that include various concrete mix ingredients and age. The target variable for all models is the compressive strength of concrete. The assessment of model performance, based on metrics like mean squared error (MSE) and R² values, was conducted on previously unseen test data. The optimal configuration for the hidden layers in the model was identified as five layers, containing 12, 16, 16, 40, and 26 neurons, respectively. The optimal learning rate for the model was 0.001. With this set of optimal hyperparameters, the best DNN model achieved an MSE of 28.76 and an R² value of 0.89 on the testing data. The results demonstrate a significant improvement in the DNN models' performance when trained with the optimized hyperparameters. In comparison to the regression model, the performance of the DNN models was significantly better. Adopting the predictive models developed in this research offers the potential for substantial cost and time savings by circumventing the need for labour-intensive and time-consuming laboratory tests. [ABSTRACT FROM AUTHOR]

The relationship between Earnings Management (EM) and Corporate Social Responsibilities (CSR) has raised a considerable number of attentions, especially, in financial and accounting field since they determine firms' performance as well as market positioning. On the contrary, in an emerging country as Vietnam, the number of relevant studies are rare together with several limitations. Thus, this paper is the pioneer investigating the cause and effect among Accruals Earnings Management (AEM), Real Earnings Management (REM) as two proxies for EM, and CSR. Next, Granger's causality replacing for traditional method is employed in order to interpret this nexus. The analysis relies on the sample size of 225 companies during the period 2014–2018 from Hanoi Stock Exchange (HNX) and Ho Chi Minh Stock Exchange (HOSE). We figured out executives prefer REM to AEM when implementing earnings manipulations. Consistent with the expectations and prior scholars, we found there is a negative correlation in the association of AEM and CSR while it is positive in the case of REM and CSR. Besides the consistent results with multivariate regression analysis (MRA) in terms of CSR-EM association, the fsQCA findings also explore more combinations regarding the relationship between CSR-EM. Additionally, all empirical results also provide evidence that there is cause-and-effect relationship in terms of AEM, REM and CSR which has never been researched and discussed over the past several years. [ABSTRACT FROM AUTHOR]

Gender poverty differences in households are likely to affect female-headed households more than male-headed households. This paper examined the evolution of the gender poverty rate gap and identified the factors that underlie differences in poverty rates between female-headed households and male-headed households using the most recent representative household surveys conducted by the Kenya National Bureau of Statistics in 2005/06 and 2015/16. An extended Blinder-Oaxaca decomposition analysis with nonlinear regression was performed. The findings indicate that poverty rates for female-headed households and male-headed households declined from 38.56 to 32.73% in 2005/06 to 30.23 and 26.04% in 2015/16, respectively. Although female-headed households (1.12) have a higher chance of falling into poverty than male-headed households (0.95), the decline in the poverty rate was higher for female-headed households (8.33%) than for male-headed households (6.69%). Therefore, the results do not support the feminization of poverty hypothesis in Kenya. Factors that have bridged the gender poverty gap include cash transfers that explain 11.02% of the gaps, literacy (53.97%), university education (10.39%), secondary education (40.84%), employment in public and private sectors (26.66%) and business employment (10.58%). Recommended policies include the implementation of the gender policy and affirmative action, enhancing literacy levels, and secondary and university enrolment. [ABSTRACT FROM AUTHOR]

AbstractThis paper proposes an innovative double power-enhanced testing procedure for inference on high-dimensional linear hypotheses in high-dimensional regression models. Through a projection approach that aims to separate useful inferential information from the nuisance one, our proposed test accurately accounts for the impact of high-dimensional nuisance parameters. We discover that with a carefully-designed projection matrix, the projection procedure enables us to transform the problem of interest into a test on moment conditions, from which we construct a U-statistic-based test that is applicable in simultaneous inference on a diverging number of linear hypotheses. We prove that under regularity conditions, the plug-in test statistic converges to its oracle counterpart, acting as well as if the nuisance parameters were known in advance. Moreover, we introduce an implementation-friendly version to tackle the computational challenge. Asymptotic null normality is established to provide convenient tools for statistical inference, accompanied by rigorous power analysis. To further strengthen the testing power, we develop two power enhancement techniques to boost the power from two distinct aspects respectively, and integrate them into one powerful testing procedure to achieve double power enhancement. The finite-sample performance is demonstrated using simulation studies, and an empirical analysis of a real data example. [ABSTRACT FROM AUTHOR]

The author reacts to comments made to his article. Perhaps Professor C. Roseman has overstated his case a bit when he asserts that three aspects of the author's paper may have been "quite misleading." Perhaps Roseman's concern about the theory of stepwise regression may be valid, but the author is happy to be able to set his mind at ease about the real world evidence with which he dealt. Perhaps Roseman has fallen into the beguiling trap of confusing statistical association with cause-and-effect explanation. Certainly population continued to increase in many resort areas during the 1970s, but one must entertain serious doubts about anyone who tries to ex- plain population increase by counting the number of empty houses.

Sci-tech and finance plays an increasingly important role and have become an important driving force in economic development. In China, the problem of insufficient financial support for sci-tech innovation is important to enterprises. According to the internal relationship between different stages of Sci-tech and the finance system, this paper is aimed at exploring the efficiency measurement method between sci-tech and finance systems. Firstly the multi-stage collaborative structure of sci-tech finance is built, where the system of sci-tech is divided into three stages including the R&D stage, transformation stage of sci-tech achievements and industrialization stage, and the financing channel is the input of the finance system into the sci-tech system at different stages. The measurement method of the multi-stage collaborative efficiency between sci-tech and finance systems is put forward by the framework of network DEA. Then, taking China as an example, we collect the information of 30 provinces and cities from 2009 to 2016 and measure the efficiency of each system and the collaborative efficiency of the both. The efficiency's spatial correlation is tested by means of Moran index. Finally, the influencing factors of the collaborative efficiency are analyzed based on the spatial econometric regression model, which considers the financing channels and human capital. To sum up, there are significant differences in the sci-tech finance collaborative efficiency among regions in China. Among them, the collaborative efficiency of Beijing, Shanghai and Jiangsu ranks in the top three. Comparing the different stages of the sci-tech system, the commercialization stage is a weak link in many regions of China. Human capital and financing channels of sci-tech finance have different degrees of positive impact on the sci-tech finance collaborative efficiency. Among them, human capital plays a greater role in promoting the sci-tech finance collaborative development. [ABSTRACT FROM AUTHOR]

In many situations, it is common to have more than one observation per experimental unit, thus generating the experiments with repeated measures. In the modeling of such experiments, it is necessary to consider and model the intra-unit dependency structure. In the literature, there are several proposals to model positive continuous data with repeated measures. In this paper, we propose one more with the generalization of the beta prime regression model. We consider the possibility of dependence between observations of the same unit. Residuals and diagnostic tools also are discussed. To evaluate the finite-sample performance of the estimators, using different correlation matrices and distributions, we conducted a Monte Carlo simulation study. The methodology proposed is illustrated with an analysis of a real data set. Finally, we create an R package for easy access to publicly available the methodology described in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the segment and weight prediction problems are investigated for ear of grape based on deep learning technologies. The image datum is collected from ZaoHeiBao grape in a greenhouse by camera. The grape ear target segmentation model is constructed by cross combining three backbone networks (ResNet18, ResNet50, and ResNet101) and four deep learning semantic segmentation networks (SFNet, GCNet, EMANet, and Deeplabv3). The experimental results show that for the SFNet-ResNet18 model, whose structural size is 52.68MB, the mean Intersection over Union (mIoU) is 79.45 % , the mean Pixel Accuracy (mPA) is 92.58 % , and the average segmentation speed of the image ( 4000 × 3000) is 0.217s. Therefore, the performance of the SFNet-ResNet18 model outperforms other combined network models and is selected to segment grape ears. Furthermore, on the basis of the segmentation results of grape ears by using the SFNet-ResNet18 model, the grape ear weight is predicted by adopting fractional regression model. The value of R 2 is 0.8903, which means that the selected model could accurately predict the weight of grape ears. The proposed method can not only segment the grape ears and accurately predict the weight of the grape ears, but also provide theoretical and technical support for grape yield prediction. [ABSTRACT FROM AUTHOR]

Applications of circular regression models are ubiquitous in many disciplines, particularly in meteorology, biology and geology. In circular regression models, variable selection problem continues to be a remarkable open question. In this paper, we address variable selection in linear-circular regression models where uni-variate linear dependent and a mixed set of circular and linear independent variables constitute the data set. We consider Bayesian lasso which is a popular choice for variable selection in classical linear regression models. We show that Bayesian lasso in linear-circular regression models is not able to produce robust inference as the coefficient estimates are sensitive to the choice of hyper-prior setting for the tuning parameter. To eradicate the problem, we propose a robustified Bayesian lasso that is based on an empirical Bayes (EB) type methodology to construct a hyper-prior for the tuning parameter while using Gibbs Sampling. This hyper-prior construction is computationally more feasible than the hyper-priors that are based on correlation measures. We show in a comprehensive simulation study that Bayesian lasso with EB-GS hyper-prior leads to a more robust inference. Overall, the method offers an efficient Bayesian lasso for variable selection in linear-circular regression while reducing model complexity. [ABSTRACT FROM AUTHOR]

The aims of the present work were to determine by meta-analysis the responses of broiler performance to increasing dietary levels of black soldier fly larvae meal (BSFLM) and to develop prediction models of the apparent metabolisable energy corrected to zero nitrogen balance (AMEn) of BSFLM. Eighteen studies from 12 countries that sum up to 20 experiments and involved a total of 63 treatments with 468 replicates and 4229 birds fulfilled the requirements for the meta-analysis. Four papers from three countries that investigated eight different samples of BSFLM were selected for development of AMEn prediction by means of regression analysis or artificial neural network (ANN) modelling. Growth performance results were not affected (p >.05) by the presence of BSFLM at levels of 5.7 ± 2.3 and 15.5 ± 6.0% in the diet. Multiple linear regression and ANN models showed high precision and accuracy, were unbiased and performed almost identically in the evaluation dataset (R2 = 0.856 and 0.858 and root mean square error of prediction as percentage of the observed mean = 5.216 and 5.241%). Feeding broilers with diets containing 15% BSFLM is not expected to have negative effects on growth performance. The following regression model could be useful for the estimation of AMEn in BSFLM: AMEn = 4.08 (± 0.330) + 0.295 (± 0.0158) × GE + 0.186 (± 0.0021) × EE; where AMEn and gross energy (GE) are MJ/kg dry matter and crude fat (EE) is % dry matter. Feeding broilers 15% black soldier fly larvae meal in the diet does not affect performance results. Metabolisable energy value of black soldier fly larvae meal can be accurately predicted from its composition. [ABSTRACT FROM AUTHOR]

This paper aims to understand the impact of demonetisation on the returns of listed firms in the NSE, as well as changes to their corresponding industry level systematic risk. Firstly, this paper examined a larger sample of Indian listed firms and a broader group of industries in its analysis compared to prior studies. Secondly, the changes to systematic risk due to demonetisation across industries with listed firms was measured. The study uses event study methodology over a thirteen-day event window (six days before and six days after the announcement) to test for abnormal returns across 1,054 listed firms. A regression analysis of 57 industry level returns is performed to test for changes in systematic risk. Significant negative abnormal returns are found for over 100 firms and 12 out of the 57 industry divisions in the sample. The same group of industries also show an increase in their systematic risk in the short run. This indicates that parts of the formal economy were also seriously affected by demonetisation. However, our paper does not find any long-term effect due to demonetisation. [ABSTRACT FROM AUTHOR]

The paper investigates whether eco-innovation, sustainability, and asset pricing effect of maximum daily return (maxing out effect), truly and steadily exists in A share market of China and the nexus among maxing out effect and idiosyncratic volatility in China's A Share Market. Regression analyses were conducted using the data of listed companies in China's A share market from July 1993 to May 2021. The empirical results show that the maxing out effect, eco-innovation and sustainability steadily exists in a sample country. In practice, this research help to improve asset pricing efficiency, provide high-quality materials for investor education, guide arbitrage trading activities, and improve resource allocation efficiency in the stock market. [ABSTRACT FROM AUTHOR]

Clustered current status data are frequently encountered in biomedical research and other areas that require survival analysis. This paper proposes graphical and formal model assessment procedures to evaluate the goodness of fit of the additive hazards model to clustered current status data. The test statistics proposed are based on sums of martingale-based residuals. Relevant asymptotic properties are established, and empirical distributions of the test statistics can be simulated utilizing Gaussian multipliers. Extensive simulation studies confirmed that the proposed test procedures work well for practical scenarios. This proposed method applies when failure times within the same cluster are correlated, and in particular, when cluster sizes can be informative about intra-cluster correlations. The method is applied to analyze clustered current status data from a lung tumorigenicity study. [ABSTRACT FROM AUTHOR]

Small-scale broiler farming has become one of the fastest-growing sub-sectors in Zimbabwe. However, the profitability levels of these small ventures have been questioned due to the rising production costs owing to the highly inflationary macroeconomic environment. Using a sample of 110 small-scale broiler producers selected using the exponential non-discriminative snowballing sampling method, the paper analysed the factors affecting these farmers' profitability. Gross margin analysis and quantile regression were used to analyse the data. We found that, although feed costs constituted 56.8% of the total variable costs, small-scale broiler production is a profitable venture in the area with a mean gross margin of US$ 65.25 per batch of 100 broilers and a return per dollar variable costs invested of $ 1.15. Training on broiler production, farming experience, level of education, access to extension services, access to credit and household size significantly determine the profitability of the broiler enterprise at various quantiles, with only training affecting profitability at all three quantiles. All these factors are essential contributing factors to the profitability or lack thereof of small-scale broiler production in Mutare district. We recommend that training programs on effective broiler production be offered to small-scale farmers in the Mutare district. The government and non-governmental organisations should also develop small loan packages that can assist the farmers in improving their production levels and profits. Government extension workers should be more visible and accessible to the farmers in peri-urban areas to positively influence their profitability levels. [ABSTRACT FROM AUTHOR]

This study aims to determine the impact of Corporate Governance on the relationship between the macro and micro factors causing financial distress in 240 Vietnamese listed non-financial firms. The study also investigates the marginal benefits of different corporate governance practices by applying an endogenous switching regression model (ESRM). The research clarifies that a firm with strong corporate governance practice has a low probability of financial distress compared to a weak corporate governance firm. Moreover, the risk of financial distress is significantly reduced when improving the corporate governance practice. This paper contributes empirical evidence on the predominant benefit of strong corporate governance practices and marginal benefit in risk mitigation in enhancing corporate governance. The article suggests that Vietnamese firms should implement strong corporate governance to overcome the risk of financial distress. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between family ownership and firm performance over the period 2009–2017 for a large sample of French-listed firms. Previous research showed that family ownership can bring both benefits and costs to firms. Empirical results in whether it enhances or undermines firm performance are inconclusive. This paper aims to further our understanding of the complex relationship between family ownership and firm performance. It clarifies how family owners influence on firm performance depends on their ownership levels. By performing Panel Smooth Transition Regression model (PSTR), we find that the relationship between family ownership and firm performance is non-linear. The model has one threshold at the 37.62% of family ownership and two extreme regimes. The results show that below the threshold, the relation is negative. Family members have fewer incentives to bear the cost of effective monitoring. However, above the estimated threshold, family ownership has a positive impact on firm performance. This paper supports the view that family owners are more motivated to enhance performance when they hold large stake of capital in the firm as family wealth is closely related to firm profitability. These findings provide useful insights for investors seeking investment opportunities in firms with family ownership as the latter constitute a large proportion of publicly listed firms in the world. [ABSTRACT FROM AUTHOR]

Walking within neighbourhoods had been found to be beneficial in many ways including health, but there is the need to consider factors that will influence walking behaviour for planning purposes. Finding factors of walkability requires more than one tool of measurement since it involves urban form and socio-economy. The space syntax method had been applied in examining urban form (street structure) and the socio-economic factor was measured using regression analysis, within neighbourhoods of contrasting characteristics. This paper examines the syntactical properties relating to the new urbanist area and core area of the city, relating to their walking behaviour, examining the people's socio-economic characteristics, investigating the individuals' walking behaviour and factors explaining the variance of walkability in the study area. The paper concludes by using the findings to recommend strategies that planners, designers, and policy-makers can use to design successful transit and pedestrian-oriented developments. [ABSTRACT FROM AUTHOR]

Among the models applied to analyze survival data, a standout is the inverse Gaussian distribution, which belongs to the class of models to analyze positive asymmetric data. However, the variance of this distribution depends on two parameters, which prevents establishing a functional relation with a linear predictor when the assumption of constant variance does not hold. In this context, the aim of this paper is to re-parameterize the inverse Gaussian distribution to enable establishing an association between a linear predictor and the variance. We propose deviance residuals to verify the model assumptions. Some simulations indicate that the distribution of these residuals approaches the standard normal distribution and the mean squared errors of the estimators are small for large samples. Further, we fit the new model to hospitalization times of COVID-19 patients in Piracicaba (Brazil) which indicates that men spend more time hospitalized than women, and this pattern is more pronounced for individuals older than 60 years. The re-parameterized inverse Gaussian model proved to be a good alternative to analyze censored data with non-constant variance. [ABSTRACT FROM AUTHOR]

Local Linear Regression (LLR) is a nonparametric regression model applied in the modeling phase of Response Surface Methodology (RSM). LLR does not make reference to any fixed parametric model. Hence, LLR is flexible and can capture local trends in the data that might be too complicated for the OLS. However, besides the small sample size and sparse data which characterizes RSM, the performance of the LLR model nosedives as the number of explanatory variables considered in the study increases. This phenomenon, popularly referred to as curse of dimensionality, results in the scanty application of LLR in RSM. In this paper, we propose a novel locally adaptive bandwidths selector, unlike the fixed bandwidths and existing locally adaptive bandwidths selectors, takes into account both the number of the explanatory variables in the study and their individual values at each data point. Single and multiple response problems from the literature and simulated data were used to compare the performance of the L L R P A B with those of the OLS, L L R F B and L L R A B . Neural network activation functions such ReLU, Leaky-ReLU, SELU and SPOCU was considered and give a remarkable improvement on the loss function (Mean Squared Error) over the regression models utilized in the three data. [ABSTRACT FROM AUTHOR]

In recent decades, the use of regression models with random effects has made great progress. Among these models' attractions is the flexibility to analyze correlated data. In various situations, the distribution of the response variable presents asymmetry or bimodality. In these cases, it is possible to use the normal regression with random effect at the intercept. In light of these contexts, i.e. the desire to analyze correlated data in the presence of bimodality or asymmetry, in this paper we propose a regression model with random effect at the intercept based onthe generalized inverse Gaussian distribution model with correlated data. The maximum likelihood is adopted to estimate the parameters and various simulations are performed for correlated data. A type of residuals for the new regression is proposed whose empirical distribution is close to normal. The versatility of the new regression is demonstrated by estimating the average price per hectare of bare land in 10 municipalities in the state of São Paulo (Brazil). In this context, various databases are constantly emerging, requiring flexible modeling. Thus, it is likely to be of interest to data analysts, and can make a good contribution to the statistical literature. [ABSTRACT FROM AUTHOR]

Dimensional analysis (DA) is a well-developed, widely-employed methodology in the physical and engineering sciences. The application of dimensional analysis in statistics leads to three advantages: (1) the reduction of the number of potential causal factors that we need to consider, (2) the analytical insights into the relations among variables that it generates, and (3) the scalability of results. The formalization of the dimensional-analysis method in statistical design and analysis gives a clear view of its generality and overlooked significance. In this paper, we first provide general procedures for dimensional analysis prior to statistical design and analysis. We illustrate the use of dimensional analysis with three practical examples. In the first example, we demonstrate the basic dimensional-analysis process in connection with a study of factors that affect vehicle stopping distance. The second example integrates dimensional analysis into the regression analysis of the pine tree data. In our third example, we show how dimensional analysis can be used to develop a superior experimental design for the well-known paper helicopter experiment. In the regression example and in the paper helicopter experiment, we compare results obtained via the dimensional-analysis approach to those obtained via conventional approaches. From those, we demonstrate the general properties of dimensional analysis from a statistical perspective and recommend its usage based on its favorable performance. [ABSTRACT FROM AUTHOR]

This article studies a general regression model for a scalar quality response with mixed types of process predictors including process images, functional sensing signals, and scalar process setup attributes. To represent a set of time-dependent process images, a third-order tensor is employed for preserving not only the spatial correlation of pixels within one image but also the temporal dependency among a sequence of images. Although there exist some papers dealing with either tensorial or functional regression, there is little research to thoroughly study a regression model consisting of both tensorial and functional predictors. For simplicity, the presented regression model is called functional linear regression with tensorial and functional predictor (FLR-TFP). The advantage of the presented FLR-TFP model, which is compared to the classical stack-up strategy, is that FLR-TFP can handle both tensorial and functional predictors without destroying the data correlation structure. To estimate an FLR-TFP model, this article presents a new alternating Elastic Net (AEN) estimation algorithm, in which the problem is reformed as three sub-problems by iteratively estimating each group of tensorial, functional, and scalar parameters. To execute the proposed AEN algorithm, a systematic approach is developed to effectively determine the initial running sequence among three sub-problems. The performance of the FLR-TFP model is evaluated using simulations and a real-world case study of friction stir blind riveting process. [ABSTRACT FROM AUTHOR]

In this paper, by virtual of the inverse probability weighted technique, we considered the jump-preserving estimation on the nonparametric regression models with missing data on response variable. First, we used local piecewise-linear expansion respectively with left and right kernel to approximate the unknown regression function. Second, we obtained the left- and right-limit estimation of regression function at each observed points and then determinated the final estimators by residual sums of squares. Third, we presented the convergence rate of estimators and the residual sums of squares. Finally, we illustrated the performance of our proposed method through some simulation studies and a conjunctivitis example from The Affiliated Hospital of Hangzhou Normal University. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new distribution, named unit log–log distribution, defined on the bounded (0,1) interval. Basic distributional properties such as model shapes, stochastic ordering, quantile function, moments, and order statistics of the newly defined unit distribution are studied. The maximum likelihood estimation method has been pointed out to estimate its model parameters. The new quantile regression model based on the proposed distribution is introduced and it has been derived estimations of its model parameters also. The Monte Carlo simulation studies have been given to see the performance of the estimation method based on the new unit distribution and its regression modeling. Applications of the newly defined distribution and its quantile regression model to real data sets show that the proposed models have better modeling abilities than competitive models. The proposed unit quantile regression model has targeted to explain linear relation between educational measurements of both OECD (Organization for Economic Co-operation and Development) countries and some non-members of OECD countries, and their Better Life Index. The existence of the significant covariates has been seen on the real data applications for the unit median response. [ABSTRACT FROM AUTHOR]

In this paper, field spectroradiometer and aboveground biomass (AGB) data were acquired at the harvest stage at two sites in semiarid grasslands in Inner Mongolia, China. Four forms of commonly used vegetation indices (VIs) using all possible combinations of narrow-band first derivative (FDR) and raw reflectance (RR) were calculated, and the best FDR-VIs and RR-VIs were chosen by a linear regression analysis against AGB. The stepwise multiple linear regression (SMLR) models using the optimal FDR-VIs, RR-VIs, and both FDR-VIs and RR-VIs as input variables were developed for estimating the AGB. Results demonstrated that the estimation performance using the best FDR-VIs were comparable with the best RR-VIs, while the accuracy has been further improved by combining the best FDR-VIs and RR-VIs (maximum decrease in RMSE of 44% and minimum RMAE of 4.7%). The approach was found to be an important step for more accurate and effective grassland AGB estimation. [ABSTRACT FROM AUTHOR]

In this paper, we study the properties of a special class of frailty models when the frailty is common to several failure times. The models are closely linked to Archimedean copula models. We establish a useful formula for cumulative baseline hazard functions and develop a new estimator for cumulative baseline hazard functions in bivariate frailty regression models. Based on our proposed estimator, we present a graphical model checking procedure. We fit a leukemia data set using our model and end our paper with some discussions. [ABSTRACT FROM AUTHOR]

This paper discusses methods of estimating the reproductive power and the accompanying survival function of communicable events, e.g. infectious disease transmission. The early stage of an outbreak can be described by the infectiousness of the outbreak process, but in later stages of the outbreak, this is complicated by factors such as changing contact patterns and the impact of control measures. It is important to take these factors into account in order to get a good, if approximate, model for an outbreak process. This paper proposes a non-homogeneous birth process and regression model for the reproductive power function, similar to models in discrete survival analysis. A baseline reproductive power function gives a description of the outbreak when covariates are at their baseline values. As an illustration these methods are applied to an avian influenza (H5N1) outbreak among poultry in Thailand. [ABSTRACT FROM AUTHOR]

Organisations are increasingly relying on supplier development as a supply chain strategy to improve organisational performance through knowledge transfer. Prior research suggests that effective knowledge transfer requires absorptive capacity, allowing organisations to identify, assimilate and convert external knowledge into commercial value. The purpose of this paper is twofold. Firstly, to examine the effectiveness of knowledge transfer in institutionalised supplier development and, secondly, to examine the mediating effect of absorptive capacity on the relationship between knowledge transfer and operational performance. We used multiple hierarchical regression and mediation analyses using a bootstrapping procedure to analyse 171 responses from self-administered questionnaires from stratified randomly selected small and medium-sized local contractors in Zambia's construction industry. Findings reveal that knowledge transfer has a positive influence on the operational performance of local contractors. The study also found that knowledge transfer improves absorptive capacity, which significantly improves operational performance. The study shows that some of the dimensions of absorptive capacity do not significantly mediate the relationship. However, the relationship between knowledge transfer and the operational performance of local contractors is mediated by overall absorptive capacity. The paper contributes to understanding the role of absorption capacity in knowledge transfer and performance in the construction industry. Besides, the findings confirm that the effect of absorption capacity as a whole is more significant than its dimensions. Policy and managerial implications of the study findings have also been advanced. [ABSTRACT FROM AUTHOR]

Researchers in statistical shape analysis often analyze outlines of objects. Even though these contours are infinite-dimensional in theory, they must be discretized in practice. When discretizing, it is important to reduce the number of sampling points considerably to reduce computational costs, but to not use too few points so as to result in too much approximation error. Unfortunately, determining the minimum number of points needed to achieve sufficiently approximate the contours is computationally expensive. In this paper, we fit regression models to predict these lower bounds using characteristics of the contours that are computationally cheap as predictor variables. However, least squares regression is inadequate for this task because it treats overestimation and underestimation equally, but underestimation of lower bounds is far more serious. Instead, to fit the models, we use the LINEX loss function, which allows us to penalize underestimation at an exponential rate while penalizing overestimation only linearly. We present a novel approach to select the shape parameter of the loss function and tools for analyzing how well the model fits the data. Through validation methods, we show that the LINEX models work well for reducing the underestimation for the lower bounds. [ABSTRACT FROM AUTHOR]

This paper introduces a new class of efficient and debiased two-step shrinkage estimators for a linear regression model in the presence of multicollinearity. We derive the proposed estimators' mean square error and define the necessary and sufficient conditions for superiority over the existing estimators. In addition, we develop an algorithm for selecting the shrinkage parameters for the proposed estimators. The comparison of the new estimators versus the traditional ordinary least squares, ridge regression, Liu, and the two-parameter estimators is done by a matrix mean square error criterion. The Monte Carlo simulation results show the superiority of the proposed estimators under certain conditions. In the presence of high but imperfect multicollinearity, the two-step shrinkage estimators' performance is relatively better. Finally, two real-world chemical data are analyzed to demonstrate the advantages and the empirical relevance of our newly proposed estimators. It is shown that the standard errors and the estimated mean square error decrease substantially for the proposed estimator. Hence, the precision of the estimated parameters is increased, which of course is one of the main objectives of the practitioners. [ABSTRACT FROM AUTHOR]

Learning-Enabled Components (LECs), such as neural networks, are broadly employed in Cyber-Physical Systems (CPSs) to tackle a wide variety of complex tasks in high-uncertainty environments. However, the training dataset is inevitably incomplete, and Out-Of-Distribution (OOD) data not encountered during the LEC training may lead to erroneous predictions, jeopardizing the safety of the system. In this paper, we first analyze the causes of OOD data and define various types of OOD data in learning- enabled CPSs. We propose an approach to effectively detect OOD data for both classification and regression problems. The proposed approach incorporates the variational autoencoder for classification and regression model to the Inductive Conformal Anomaly Detection (ICAD) framework, enabling the detection algorithm to take into consideration not only the LEC inputs but also the LEC outputs. We evaluate the approach using extensive experiments for both classification and regression tasks, and the experimental results validate the effectiveness of the proposed method for detecting different types of OOD data. Furthermore, the execution time of detection is relatively short; therefore, the proposed approach can be used for real-time detection. [ABSTRACT FROM AUTHOR]

Many of the datasets in real-world applications contain incompleteness. In this paper, we approach the effects and possible solutions to incomplete databases in regression, aiming to bridge a gap between theoretically effective algorithms. We investigated the actual effects of missing data for regression by analyzing its impact in several publicly available databases implementing popular algorithms like Decision Tree, Random Forests, Adaboost, K-Nearest Neighbors, Support Vector Machines, and Neural Networks. Our goal is to offer a systematic view of how missing data may affect regression results. After exhaustive simulation analyzing eight public data-sets from UCI and KEEL (Abalone, Arfoil, Bike, California, Compactiv, Mortage, Wankara and Wine), we concluded that the effect of missing data may be significant. The results obtained showed that K-Nearest Neighbors works better than others in the regression of data that has missing data. [ABSTRACT FROM AUTHOR]

Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the ultra high-dimensional setting. The challenge is even more pronounced when the individual-level data cannot be shared across studies, known as DataSHIELD contraint. Under sparse regression models that are assumed to be similar yet not identical across studies, we propose in this paper a novel integrative estimation procedure for data-Shielding High-dimensional Integrative Regression (SHIR). SHIR protects individual data through summary-statistics-based integrating procedure, accommodates between-study heterogeneity in both the covariate distribution and model parameters, and attains consistent variable selection. Theoretically, SHIR is statistically more efficient than the existing distributed approaches that integrate debiased LASSO estimators from the local sites. Furthermore, the estimation error incurred by aggregating derived data is negligible compared to the statistical minimax rate and SHIR is shown to be asymptotically equivalent in estimation to the ideal estimator obtained by sharing all data. The finite-sample performance of our method is studied and compared with existing approaches via extensive simulation settings. We further illustrate the utility of SHIR to derive phenotyping algorithms for coronary artery disease using electronic health records data from multiple chronic disease cohorts. [ABSTRACT FROM AUTHOR]

Supersaturated multistratum designs are applied for identifying important factors in experiments in which the run order cannot be completely randomized. Since supersaturated multistratum designs have small run sizes and large numbers of factors, there exist problems of model uncertainty. A drawback of the stepwise regression analysis commonly used in the literature is that it only produces a single model and, thus, is not suitable to deal with model uncertainty. In this paper, we propose a Bayesian approach for analyzing the data collected from supersaturated multistratum designs. Instead of producing a single model, the Bayesian analysis reports several competing models and, thus, provides an opportunity for the experimenters to explore potentially important factors. To further reduce uncertainty, we suggest conducting follow-up experiments and develop a generalized model-discrimination criterion for selecting follow-up supersaturated designs that are effective in reducing ambiguity in the analysis results. [ABSTRACT FROM AUTHOR]

Courageous behaviors are risky and devoted actions performed primarily for the benefit of others, and they closely relate to many beneficial organizational (e.g., commitment) outcomes. Even courage plays a crucial role in many professions' results; investigating it in military content is a primary issue. This paper examined the relationship between the Big Five personality traits and organizational commitment and the moderator role of courage in this relationship by focusing on coast guards. Cross-sectional survey data (n = 512) were obtained from employees and analyzed using the least square method regression analysis. The results showed that the Conscientiousness trait is a strong antecedent for organizational commitment, and courage emerges as a moderator for the relationship between personality traits and organizational commitment. High courage strengthened the effect of Conscientiousness-Emotional Stability on normative commitment, Extraversion-Agreeableness on affective commitment, Openness on continuance, and normative commitment. Practitioners might recruit high conscientious, agreeable, emotionally stable, and courageous candidates considering correlational and moderating effects. [ABSTRACT FROM AUTHOR]

According to the Atlas of Human Development in Brazil, the income dimension of Municipal Human Development Index (MHDI-I) is an indicator that shows the population's ability in a municipality to ensure a minimum standard of living to provide their basic needs, such as water, food and shelter. In public policy, one of the research objectives is to identify social and economic variables that are associated with this index. Due to the income inequality, evaluate these associations in quantiles, instead of the mean, could be more interest. Thus, in this paper, we develop a Bayesian variable selection in quantile regression models with hierarchical random effects. In particular, we assume a likelihood function based on the Generalized Asymmetric Laplace distribution, and a spike-and-slab prior is used to perform variable selection. The Generalized Asymmetric Laplace distribution is a more general alternative than the Asymmetric Laplace one, which is a common approach used in quantile regression under the Bayesian paradigm. The performance of the proposed method is evaluated via a comprehensive simulation study, and it is applied to the MHDI-I from municipalities located in the state of Rio de Janeiro. [ABSTRACT FROM AUTHOR]

This paper introduces a new class of distributions by compounding the inverse Lindley distribution and power series distributions which is called compound inverse Lindley power series (CILPS) distributions. An important feature of this distribution is that the lifetime of the component associated with a particular risk is not observable, rather only the minimum lifetime value among all risks is observable. Further, these distributions exhibit an unimodal failure rate. Various properties of the distribution are derived. Besides, two special models of the new family are investigated. The model parameters of the two sub-models of the new family are obtained by the methods of maximum likelihood, least square, weighted least square and maximum product of spacing and compared them using the Monte Carlo simulation study. Besides, the log compound inverse Lindley regression model for censored data is proposed. Three real data sets are analyzed to illustrate the flexibility and importance of the proposed models. [ABSTRACT FROM AUTHOR]

Background: The Asenze study has the long-term goal of promoting better physical, cognitive and psychosocial functioning of children in a rural area in KwaZulu-Natal, 50 km from Durban, with a view to planning interventions to promote growth and development for very young children. The specific objective in this paper was to provide information for the Child Health and Development project of the Valley Trust to assist with intervention planning. The broader goal was to assess developmental delays in communities ravaged by the HIV epidemic. The Asenze study was designed in two phases from 2008 and 2012. The current paper reports on 1 581 4–6-year-old children in the baseline phase (2008–2010) in the five adjacent tribal areas in the study area. Method: The participants included all the 4–6-year-olds whose parents had consented to inclusion in the project and their caregivers. Data were derived from a brief questionnaire administered in the homes of participants, and subsequently from medical and psychological assessments of the children and their caregivers at the Asenze clinic. The association between child factors and other factors (geographic area, socioeconomic status (SES), parental level of education, the child's preschool education) on the one hand, and the child's cognitive performance (as measured by the Grover Counter and subtests of the KABC-11) were analysed. Linear regression models were employed to determine which predictor variables of interest in a model were associated with the children's cognitive scores as the dependent variables. Results: Based on the data, the principal factors associated with children's cognitive outcomes were height-for-age z-score (HAZ), preschool education and the area of residence. Generally children who had low cognitive scores were more often stunted (as defined by the WHO anthropometric tables), had not had preschool education, and came from areas less favourable in terms of local infrastructure and access to employment opportunities and arable land. Conclusion: The finding from this cross-sectional analysis of baseline data showed that in addition to height for age and preschool education, which are commonly thought to impact on cognition, the local authority area where the children lived was associated with their scores on cognitive tests. This has implications for intervention planning. The functioning of local government in promoting the type of community development that will protect the rights of children should be taken into account. [ABSTRACT FROM AUTHOR]

In this paper, we consider a regression model under the generalized Waring distribution for modeling count data. We develop and implement local influence diagnostic techniques based on likelihood displacement. Also we develop case-deletion methods. The generalized Waring regression model is presented as a mixture of the Negative Binomial and the Beta II distributions, and it is compared to the Negative Binomial and Waring regression models. Estimation is performed by maximum likelihood function. The influence measures developed in this paper are applied to a Spanish football league data set. Empirical results show that the generalized Waring regression model performs better when compared to the Negative Binomial and Waring regression models. Technical details are presented in the Appendix. [ABSTRACT FROM AUTHOR]

This paper examines the link between a company's internal conflicts and its marketplace success. In particular, it examines how conflicts between a selling firm's sales and support personnel can lead to unintended variations in its external behavior and how these variations can affect the buyer's commitment. It also examines how salespeople may use a variety of informal influence tactics to moderate the impact of these conflicts on the firm's behavior. These relationships are tested using regression analysis and data from separate samples of sales and purchasing professionals. The data support the position that internal goal conflicts lead to variations in the firm's external behavior, that this relationship is moderated by a salesperson's use of influence tactics, and that these variations have a direct effect on the buyer's commitment. [ABSTRACT FROM AUTHOR]

In the censored data exploration, the classical linear regression model which assumes normally distributed random errors is perhaps one of the commonly used frameworks. However, practical studies have often criticized the classical linear regression model because of its sensitivity to departure from the normality and partial nonlinearity. This paper proposes to solve these potential issues simultaneously in the context of the partial linear regression model by assuming that the random errors follow a scale-mixture of normal (SMN) family of distributions. The postulated method allows us to model data with great flexibility, accommodating heavy tails and outliers. By implementing the B-spline approximation and using the convenient hierarchical representation of the SMN distributions, a computationally analytical EM-type algorithm is developed for obtaining maximum likelihood (ML) parameter estimates. Various simulation studies are conducted to investigate the finite sample properties, as well as the robustness of the model in dealing with the heavy tails distributed datasets. Real-world data examples are finally analyzed for illustrating the usefulness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Population spatialization is widely used for spatially downscaling census population data to finer-scale. The core idea of modern population spatialization is to establish the association between ancillary data and population at the administrative-unit-level (AU-level) and transfer it to generate the gridded population. However, the statistical characteristic of attributes at the pixel-level differs from that at the AU-level, thus leading to prediction bias via the cross-scale modeling (i.e. scale mismatch problem). In addition, integrating multi-source data simply as covariates may underutilize spatial semantics, and lead to incorrect population disaggregation; while neglecting the spatial autocorrelation of population generates excessively heterogeneous population distribution that contradicts to real-world situation. To address the scale mismatch in downscaling, this paper proposes a Cross-Scale Feature Construction (CSFC) method. More specifically, by grading pixel-level attributes, we construct the feature vector of pixel grade proportions to narrow the scale differences in feature representation between AU-level and pixel-level. Meanwhile, fine-grained building patch and mobile positioning data are utilized to adjust the population weighting layer generated from POI-density-based regression modeling. Spatial filtering is furtherly adopted to model the spatial autocorrelation effect of population and reduce the heterogeneity in population caused by pixel-level attribute discretization. Through the comparison with traditional feature construction method and the ablation experiments, the results demonstrate significant accuracy improvements in population spatialization and verify the effectiveness of weight correction steps. Furthermore, accuracy comparisons with WorldPop and GPW datasets quantitatively illustrate the advantages of the proposed method in fine-scale population spatialization. [ABSTRACT FROM AUTHOR]