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Spatial error parametric panel model is one of the most popularly used analytical tools in spatial econometrics. Although this model takes into account the possible spatial correlation of errors, it ignores the potential serial correlation of errors and commonly existed nonlinearity between variables. These may lead to inefficient estimators and model misspecification. Therefore, this paper establishes a fixed effects semiparametric single-index panel model (SPSIPM) with spatio-temporal correlated errors. Firstly, we apply B-spline to approximate the single-index function and incorporate the information of initial period observations into quasi-likelihood function of the model to construct its profile quasi-maximum likelihood estimators (PQMLEs). Secondly, it is proved that PQMLEs of both parameters and single-index function are consistent and asymptotically normal under some mild conditions. Thirdly, we propose a nonparametric bootstrap test for examining the nonlinearity of model. Fourthly, numerical simulations reveal the estimates and test statistic have good finite sample performance. Finally, the model estimation methodology is employed to analyze the driving factors of Chinese resident real wage level. [ABSTRACT FROM AUTHOR]

Clustered data are a special type of correlated data where units within a cluster are correlated while units between different clusters are independent. The number of units in a cluster can be associated with that cluster's outcome. This is called the informative cluster size (ICS), which is known to impact clustered data inference. However, when comparing the outcomes from multiple groups of units in clustered data, investigating ICS may not be enough. This is because the number of units belonging to a particular group in a cluster can be associated with the outcome from that group in that cluster, leading to an informative intra-cluster group size or IICGS. This phenomenon of IICGS can exist even in the absence of ICS. Ignoring the existence of IICGS can result in a biased inference for group-based outcome comparisons in clustered data. In this article, we mathematically formulate the concept of IICGS while distinguishing it from ICS and propose a nonparametric bootstrap-based statistical hypothesis-testing mechanism for testing any claim of IICGS in a clustered data setting. Through simulations and real data applications, we demonstrate that our proposed statistical testing method can accurately identify IICGS, with substantial power, in clustered data. [ABSTRACT FROM AUTHOR]

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With the increase in haze pollution in Chinese cities, answering the question of whether using a high-speed rail (HSR) as a green and efficient transportation system can alleviate haze pollution in China has become a research hotspot. This study empirically tests the impact of HSR network construction on haze pollution and its spatial spillover effects. (1) The construction of a HSR would have a mitigation effect on haze pollution in node cities and surrounding cities, and the more developed the HSR is, the more significant that this effect would be. (2) Haze pollution persists for a long time, the haze pollution from the previous year may have a positive promoting effect on the haze pollution in the following year. (3) The use of a HSR reduces haze pollution by replacing traditional road transportation and promoting industrial structure upgrading and technological innovation. (4) The inhibitory effect of HSR use on haze pollution varies due to regional differences and variation in city size. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, the analysis is focused on the unbiasedness of prediction based on a linear regression model. The accuracy of the considered predictors is measured by means of a statistical test based on the ex-post prediction variance. A significantly large value of the ratio of ex-post prediction variances and residual variance leads to the rejection of the hypothesis on unbiasedness of prediction. The distribution of the ratio is assessed by means of the parametric bootstrap procedure. Moreover, bootstrap-type tests, attempting to verify the unbiasedness of three predictors of the linear regression, are proposed. The distribution of the linear regression error term is assumed to be normal or t-Student. The power of considered statistical tests is examined based on an extensive simulation analysis. [ABSTRACT FROM AUTHOR]

Clustered data are a special type of correlated data where units within a cluster are correlated while units between different clusters are independent. The number of units in a cluster can be associated with that cluster’s outcome. This is called the informative cluster size (ICS), which is known to impact clustered data inference. However, when comparing the outcomes from multiple groups of units in clustered data, investigating ICS may not be enough. This is because the number of units belonging to a particular group in a cluster can be associated with the outcome from that group in that cluster, leading to an informative intra-cluster group size or IICGS. This phenomenon of IICGS can exist even in the absence of ICS. Ignoring the existence of IICGS can result in a biased inference for group-based outcome comparisons in clustered data. In this article, we mathematically formulate the concept of IICGS while distinguishing it from ICS and propose a nonparametric bootstrap-based statistical hypothesis-testing mechanism for testing any claim of IICGS in a clustered data setting. Through simulations and real data applications, we demonstrate that our proposed statistical testing method can accurately identify IICGS, with substantial power, in clustered data.

In this article, we propose new semiparametric and bootstrap tests based on the ratio of two variances under a density ratio model and compare them with existing tests for testing the equality of two variances from two independent populations. We showed that the ratio of two independent variances follows an asymptotic log-normal distribution. Also presented in this article are large sample asymptotic results of the semiparametric estimator of the ratio of the two sample variances. We also present a theoretical comparison of our semiparametric test on the ratio of two variances with the existing non parametric test using Pitman's relative efficiency. Numerical comparisons of the performance of various tests are performed via simulation studies. We also use two numerical examples to illustrate the implementation of new tests. [ABSTRACT FROM AUTHOR]

This work proposes powerful tests using bootstrap methods for the tail index in the family of distribution functions with nondegenerate right tail. It is of interest to test whether the right tail of a distribution function is the same as or heavier than that of the Pareto distribution with index m0 for some m0, vs. alternatively the right tail is lighter. Based on the nonparametric tests of Jureckova and Picek (2001, A class of tests on the tail index, Extreme 4:2, 165–183) that use empirical distribution functions of sample maxima, the proposed bootstrap tests employ two-stage subsamplings: One is subsampling from the sample maxima to be applicable even to the heavy tailed index and the other is subsampling from collections of empirical distributions of the sample maxima. We show the first-order validity of the bootstrap for the nonparametric test and construct bootstrap test statistics. The limiting distribution of the bootstrap test statistics in the null hypothesis is established and the consistency of the bootstrap tests is verified. Applications of the proposed bootstrap tests are given to autoregressive time series models. A simulation study is conducted to see performance of the bootstrap tests and it illustrates that the proposed bootstrap tests outperform the existing ones. Real data of heavy tailed financial stock indices are applied to the proposed bootstrap tests. [ABSTRACT FROM AUTHOR]

To analyze multivariate outcomes in clinical trials, several authors have suggested generalizations of the univariate Mann–Whitney test. As the Mann–Whitney statistic compares the subjects' outcome pairwise, the multivariate generalizations are known as generalized pairwise comparisons (GPC) statistics. For GPC statistics such as the net treatment benefit, the win ratio, and the win odds, asymptotic based or re-sampling tests have been suggested in the literature. However, asymptotic methods require a sufficiently high sample size to be accurate, and re-sampling methods come with a high computational burden. We use graph theory notation to obtain closed-form formulas for the expectation and the variance of the permutation and bootstrap sampling distribution of the GPC statistics, which can be utilized to develop fast and accurate inferential tests for each of the GPC statistics. A simple example and a simulation study demonstrate the accuracy of the exact permutation and bootstrap methods, even in very small samples. As the time complexity is O (N 2) , where N is the total number of patients, the exact methods are fast. In situations where asymptotic methods have been used to obtain these variance matrices, the new methods will be more accurate and equally fast. In situations where bootstrap has been used, the new methods will be both more accurate and much faster. [ABSTRACT FROM AUTHOR]

Stochastic dominance is usually used to rank random variables by comparing their distributions, so it is widely applied in economics and finance. In actual applications, complete stochastic dominance is too demanding to meet, so relaxation indexes of stochastic dominance have attracted more attention. The π index, the biggest gap between two distributions, can be a measure of the degree of deviation from complete dominance. The traditional estimation method is to use the empirical distribution functions to estimate it. Considering the populations under comparison are generally of the same nature, we can link the populations through density ratio model under certain condition. Based on this model, we propose a new estimator and establish its statistical inference theory. Simulation results show that the proposed estimator substantially improves estimation efficiency and power of the tests and coverage probabilities satisfactorily match the confidence levels of the tests, which show the superiority of the proposed estimator. Finally we apply our method to a real example of the Chinese household incomes. [ABSTRACT FROM AUTHOR]

In epidemiological database studies, the occurrence of an event is measured with error through an indicator whose specificity is often maximised, at the expense of sensitivity. However, if the indicator has low sensitivity, measures of occurrence are underestimated. In association studies, risk difference is biased, and risk ratio may be biased as well, in either direction, if the sensitivity is differential across exposure groups. In this work, we show that if an auxiliary screening indicator can be defined to complement the main indicator, estimates of the positive predictive value of both indicators provide tools to estimate the sensitivity of the primary indicator, or a lower bound thereof. This mitigates bias in estimating the number of cases, prevalence, cumulative incidence, rate (particularly when the event is rare), and in association studies, risk ratio and risk difference. They also allow testing for non-differential sensitivity. While direct estimation of sensitivity is often infeasible, this novel methodology improves evidence based on data obtained from re-use of existing databases, which may prove critical for regulatory and public health decisions., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

This paper considers a paired data framework and discusses the question of marginal homogeneity of bivariate high-dimensional or functional data. The related testing problem can be endowed into a more general setting for paired random variables taking values in a general Hilbert space. To address this problem, a Cramér–von-Mises type test statistic is applied and a bootstrap procedure is suggested to obtain critical values and finally a consistent test. The desired properties of a bootstrap test can be derived that are asymptotic exactness under the null hypothesis and consistency under alternatives. Simulations show the quality of the test in the finite sample case. A possible application is the comparison of two possibly dependent stock market returns based on functional data. The approach is demonstrated based on historical data for different stock market indices. [ABSTRACT FROM AUTHOR]

Investigating the relationship between Gross Domestic Product and unemployment is one of the most important challenges in macroeconomics. In this paper, we compare French and German economies in terms of the dynamic linkage between these variables. In particular, we use an empirical methodology to investigate how much the relationship between Gross Domestic Product and unemployment growth rates are dynamically different in the two major European economies over the period 2003–2019. To this aim, a Vector Autoregressive model is specified for each country to jointly model the growth rate of the two variables. Then a new statistical test is proposed to assess the distance between the two estimated models. Results indicate that the dynamic linkage between Gross Domestic Product and unemployment is very similar in the two countries. This empirical evidence does not imply identical product and labor markets in France and Germany, but it ensures that in these markets there are common dynamics. This could favor the process of economic convergence between the two countries. [ABSTRACT FROM AUTHOR]

We consider a model whereby a given response variable Y following a transformation Y : = T (Y) , satisfies some classical regression equation. In this transformation model the form of the transformation is specified analytically but incorporates an unknown transformation parameter. We develop testing procedures for the null hypothesis of homoskedasticity for versions of this model where the regression function is considered either known or unknown. The test statistics are formulated on the basis of Fourier-type conditional contrasts of a variance computed under the null hypothesis against the same quantity computed under alternatives. The limit null distribution of the test statistic is studied, as well as the behaviour of the test criterion under alternatives. Since the limit null distribution is complicated, a bootstrap version is suggested in order to actually carry out the test procedures. Monte Carlo results are included that illustrate the finite-sample properties of the new method. The applicability of the new tests on real data is also illustrated. [ABSTRACT FROM AUTHOR]

Summary: We analyse individual professional forecasters' beliefs concerning the persistence of GDP shocks. Despite substantial apparent heterogeneity in perceptions, with around one half of the sample of professional forecasters believing shocks do not have permanent effects, we show that these apparent differences may be largely due to short samples and survey respondents being active at different times. When we control for these effects, using a bootstrap, we formally do not reject the null that individuals' long‐horizon expectations are interchangeable at a given point in time. When we apply the same bootstrap approach to their medium‐term expectations, we do reject the null. We explore this difference between long and medium‐horizon forecasts by decomposing revisions in forecasts into permanent and transitory components. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive survival analysis for the inverse Gaussian distribution employing Bayesian and Fiducial approaches. It focuses on making inferences on the inverse Gaussian (IG) parameters μ and λ and the average remaining time of censored units. A flexible Gibbs sampling approach applicable in the presence of censoring is discussed and illustrations with Type II, progressive Type II, and random rightly censored observations are included. The analyses are performed using both simulated IG data and empirical data examples. Further, the bootstrap comparisons are made between the Bayesian and Fiducial estimates. It is concluded that the shape parameter ( ϕ = λ / μ) of the inverse Gaussian distribution has the most impact on the two analyses, Bayesian vs. Fiducial, and so does the size of censoring in data to a lesser extent. Overall, both these approaches are effective in estimating IG parameters and the average remaining lifetime. The suggested Gibbs sampler allowed a great deal of flexibility in implementation for all types of censoring considered. [ABSTRACT FROM AUTHOR]

A new clustering method for random intervals that are measured in the same units over the same group of individuals is provided. It takes into account the similarity degree between the expected values of the random intervals that can be analyzed by means of a two-sample similarity bootstrap test. Thus, the expectations of each pair of random intervals are compared through that test and a p-value matrix is finally obtained. The suggested clustering algorithm considers such a matrix where each p-value can be seen at the same time as a kind of similarity between the random intervals. The algorithm is iterative and includes an objective stopping criterion that leads to statistically similar clusters that are different from each other. Some simulations to show the empirical performance of the proposal are developed and the approach is applied to two real-life situations. [ABSTRACT FROM AUTHOR]

Density-ratio models are receiving increasing attention, particularly because of their relationship with generalized linear models and their applications in missing-data analyses. The density-ratio assumption, however, may not be true in some applications, and an important limitation is that the standard density-ratio model does not accommodate heterogeneity within the underlying population. To address these issues, we propose a new density-ratio model that incorporates a stratification procedure and dispersion parameters. The resulting stratified density-ratio model 1) retains attractive properties of the standard density-ratio model, while allowing the density-ratio assumption to be violated for some covariate, and 2) provides a validation tool, using a Kolmogorov-Smirnov-type statistic, to check the modeling assumption. We estimate the finite-dimensional and infinite-dimensional parameters simultaneously using an efficient nonparametric maximum likelihood approach. The resulting estimators are shown to be consistent and asymptotically normal. The asymptotic covariance matrix of the estimators for the finite-dimensional parameters attains the semiparametric efficiency bound. [ABSTRACT FROM AUTHOR]

To analyze multivariate outcomes in clinical trials, several authors have suggested generalizations of the univariate Mann–Whitney test. As the Mann–Whitney statistic compares the subjects’ outcome pairwise, the multivariate generalizations are known as generalized pairwise comparisons (GPC) statistics. For GPC statistics such as the net treatment benefit, the win ratio, and the win odds, asymptotic based or re-sampling tests have been suggested in the literature. However, asymptotic methods require a sufficiently high sample size to be accurate, and re-sampling methods come with a high computational burden. We use graph theory notation to obtain closed-form formulas for the expectation and the variance of the permutation and bootstrap sampling distribution of the GPC statistics, which can be utilized to develop fast and accurate inferential tests for each of the GPC statistics. A simple example and a simulation study demonstrate the accuracy of the exact permutation and bootstrap methods, even in very small samples. As the time complexity is O(N2), where N is the total number of patients, the exact methods are fast. In situations where asymptotic methods have been used to obtain these variance matrices, the new methods will be more accurate and equally fast. In situations where bootstrap has been used, the new methods will be both more accurate and much faster.

Hand, foot, and mouth disease (HFMD) is a global infectious disease severely threatening children's health. It has been recognized that climate factors play an important role in the transmission of HFMD. In this paper, the bootstrap test in the geographically weighted regression (GWR) literature is extended to geographically and temporally weighted regression (GTWR) models for identifying homogeneous explanatory variables and spatiotemporally heterogeneous ones. The resulting mixed GTWR model is then used to investigate spatiotemporal effect of climate factors on the HFMD incidence in Inner Mongolia, China, a provincial autonomous region with extensive area and different climatic conditions. The results demonstrate that the effect of relative humidity is global over space and time, while that of air temperature, air pressure and wind speed varies spatiotemporally. The extended bootstrap test provides a solid statistical basis for model selection. The findings from the study may provide not only a deep understanding of spatiotemporal variation characteristics of the climatic effect on the HFMD incidence, but also some useful evidences for taking measures of the disease prevention and control at the county level in different seasons. [ABSTRACT FROM AUTHOR]

In several applied fields, multimodality assessment is a crucial task as a previous exploratory tool or for determining the suitability of certain distributions. The goal of this paper is to present the utilities of the R package multimode, which collects different exploratory and testing non-parametric approaches for determining the number of modes and their estimated location. Specifically, some graphical tools (SiZer map, mode tree or mode forest) are provided, allowing for the identification of mode patterns, based on the kernel density estimation. Several formal testing procedures for determining the number of modes are described in this paper and implemented in the multimode package, including methods based on the ideas of the critical bandwidth, the excess mass or using a combination of both. This package also includes a function for estimating the modes locations and different classical data examples that have been considered in mode testing literature.

This study proposes a nonparametric bootstrap-based test to compare performances between two portfolios in terms of their information ratio. This serves as an extension to the literature that tests performance between two portfolio investment strategies that uses Sharpe ratio. Monte Carlo experiments show that the test has appropriate sizes and is powerful to most of the scenarios. However, the test does not perform well in highly correlated portfolio returns, but is better when the mean of portfolio return is modeled using an autocorrelated process.

This paper deals with testing the number of components in a Birnbaum-Saunders mixture model under randomly right censored data. We focus on two methods, one based on the modified likelihood ratio test and the other based on the shortcut of bootstrap test. Based on extensive Monte Carlo simulation studies, we evaluate and compare the performance of the proposed tests through their size and power. Moreover, a power analysis is provided as a guidance for researchers to examine the factors that affect the power of the proposed tests used in detecting the correct number of components in a Birnbaum-Saunders mixture model. Finally an example of aircraftWindshield data is used to illustrate the testing procedure. [ABSTRACT FROM AUTHOR]

This study proposes a nonparametric bootstrap-based test to compare performances between two portfolios in terms of their information ratio. This serves as an extension to the literature that tests performance between two portfolio investment strategies that uses Sharpe ratio. Monte Carlo experiments show that the test has appropriate sizes and is powerful to most of the scenarios. However, the test does not perform well in highly correlated portfolio returns, but is better when the mean of portfolio return is modeled using an autocorrelated process. [ABSTRACT FROM AUTHOR]

John Sunwoo,1 Patjanaporn Chalacheva,1 Maha Khaleel,2 Payal Shah,2 Richard Sposto,3 Roberta M Kato,4 Jon Detterich,5 Lonnie K Zeltzer,6 John C Wood,1,5 Thomas D Coates,2 Michael CK Khoo1 1Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA; 2Hematology Section, Children’s Center for Cancer, Blood Disease and Bone Marrow Transplantation, Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 3Children’s Center for Cancer, Blood Disease and Bone Marrow Transplantation, Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 4Division of Pediatric Pulmonology, Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 5Division of Pediatric Cardiology, Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 6Pediatric Pain and Palliative Care Program, Department of Pediatrics, Division of Hematology-Oncology, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, USA Purpose: The purpose of this work was to noninvasively detect and quantify microvascular blood flow changes in response to externally applied pain in humans. The responsiveness of the microvasculature to pain stimulation might serve as an objective biomarker in diseases associated with altered pain perception and dysregulated vascular functions. The availability of such a biomarker may be useful as a tool for predicting outcome and response to treatments, particularly in diseases like sickle cell anemia where clinical manifestations are directly linked to microvascular perfusion. We, therefore, developed a method to distinguish the blood flow response due to the test stimulus from the blood flow measurement that also includes concurrent flow changes from unknown origins.Subjects and methods: We measured the microvascular blood flow response in 24 healthy subjects in response to a train of randomly spaced and scaled heat pulses on the anterior forearm. The fingertip microvascular perfusion was measured using laser Doppler flowmetry. The cross-correlation between the heat pulses and the blood flow response was computed and tested for significance against the null distribution obtained from the baseline recording using bootstrapping method.Results: We estimated correlation coefficients, response time, response significance, and the magnitude of vasoreactivity from microvascular blood flow responses. Based on these pain response indices, we identified strong responders and subjects who did not show significant responses.Conclusion: The cross-correlation of a random pattern of painful stimuli with directly measured microvascular flow can detect vasoconstriction responses in a noisy blood flow signal, determine the time between stimulus and response, and quantify the magnitude of this response. This approach provided an objective measurement of vascular response to pain that may be an inherent characteristic of individual human subjects, and may also be related to the severity of vascular disorders. Keywords: objective quantification of pain, randomized heat pulse train, laser Doppler flowmetry, cross-correlation, bootstrap test, vasoreactivity

Point processes are often used to model the occurrence times of different phenomena, such as heatwaves or spike trains. Many of those problems require to study the independence between nonhommogeneous point processes in time, and this work develops three families of tests to assess that hypothesis. They can be applied to different types of processes, and all together they cover a wide range of situations appearing in real problems. The first family includes two tests for Poisson processes. The second family is based on the close point distance, and the third one on cross dependence functions. An extensive simulation study of the size and power of the tests is carried out and some practical rules to select the most appropriate test in different cases, are provided. The proposed tests are demonstrated on a real data application about the occurrence of extreme heat events in three Spanish locations. [ABSTRACT FROM AUTHOR]

The homogeneity hypothesis is investigated in a location family of distributions. A moment-based test is introduced based on data collected from a ranked set sampling scheme. The asymptotic distribution of the proposed test statistic is determined and the performance of the test is studied via simulation. Furthermore, for small sample sizes, the bootstrap procedure is used to distinguish the homogeneity of data. An illustrative example is also presented to explain the proposed procedures in this paper. [ABSTRACT FROM AUTHOR]

We consider block bootstrappings for panel mean change test of the squared CUSUM test of Horváth and Hušková (J Time Ser Anal 33:631–648, 2012): the circular block bootstrapping and stationary bootstrapping. First order asymptotic null validity of the test is proved under serial and/or cross-sectional correlation. Consistency of the test under an alternative hypothesis is also proved. A Monte-Carlo experiment reveals that the existing tests of Horváth and Hušková (2012) and others have severe size distortions for serially and/or cross-sectionally correlated panels, and the block bootstrappings remedy this size distortion problem. A real data analysis illustrates the proposed method. [ABSTRACT FROM AUTHOR]

Genetic diversity among 17 Jordanian snake melon (Cucumis melo var. flexuosus) landraces was investigated using 8 AFLP primer combinations and 14 ISSR molecular markers. The eight AFLP primer combinations produced 403 bands among which 219 (54%) were polymorphic with the polymorphic information content (PIC) ranged from 92.1% to 95.5%. The Unweighted Pair Group Method with Arithmetic Average (UPGMA) classified the landraces into two groups based on Dice similarity coefficient, ranging from a high of 0.88 to a low of 0.22% similarity. Bootstrapping of the obtained AFLP-clusters ranged from 33 to 97. The 14 ISSR primers produced 63 bands, among which 27 (43%) were polymorphic, and classified the landraces into two groups ranging from a high of 0.97 to a low of 0.39% similarity. Bootstrapping of the obtained ISSR clusters ranged from 2 to 96. Both AFLP and ISSR markers were successful in amplifying informative bands for different snake melon landraces and showed moderate level of positive correlation (r = 0.71) based on Mantel test. While the principal component analysis explained 34% of the total variation, AFLP and ISSR markers were successful in characterizing and identifying potential important population sources for snake melon breeders. [ABSTRACT FROM AUTHOR]

We propose specification tests for the innovation distribution in conditional duration models. The new tests are based either on the cumulative distribution function, or on exponential transforms such as the Laplace transform and the characteristic function, or on characterizations of the innovation-distribution under test. We study the finite-sample performance of the proposed procedures in comparison with alternative tests which employ nonparametric density estimates as well as with tests based on entropy. A bootstrap version of the tests is utilized in order to study the small sample behavior of the procedures. A real-data example illustrates the applicability of our method and confirms conclusions drawn by earlier authors. [ABSTRACT FROM AUTHOR]

Widespread use of digital information technology is having a significant impact on economic growth and people’s well-being. This paper assesses the mediating role of social capital in the impact of digital information technology on multidimensional poverty. Due to differences in regional, industrial structure and other factors, digital information technology has different impacts on urban and rural residents. In this paper, the China Family Panel Studies (CFPS) database is used for data, and a mediating effect model is used to study the problem. The digital information technology use was found to enhance social capital from social network, social participation and social trust, and subsequently alleviates multidimensional poverty. The results indicate that social trust plays a more significant mediating role than social capital and social participation on the impact of digital information technology on multidimensional poverty. Furthermore, the results reveal that the use of digital information technology has varying effects on poverty reduction among different income groups, with a much higher effect on low-income groups. However, the mediating effect of social capital in high-income groups is higher than that in low-income groups. In particular, social capital in the top 25% of income groups has the highest mediating effect. In the heterogeneity analysis between urban and rural areas, it is found that the multidimensional poverty situation in urban areas is significantly better than that in rural areas, because urban residents have a higher use of digital information, and at the same time, the quality of social capital in urban and rural groups is different, leading to the difference in the effect of digital information technology on poverty reduction of multidimensional poverty. Based on the results of the study, we believe that the use of digital information technology has actually improved the multi-dimensional poverty status of all groups, and social capital plays a key mediating role in it. Relying on the progress of digital information technology, we can build more convenient social network. Establish more adequate channels of information communication, enhance social trust and social participation, and alleviate multidimensional poverty.

In this paper, a ratio test based on bootstrap approximation is proposed to detect the persistence change in heavy-tailed observations. This paper focuses on the symmetry testing problems of I(1)-to-I(0) and I(0)-to-I(1). On the basis of residual CUSUM, the test statistic is constructed in a ratio form. I prove the null distribution of the test statistic. The consistency under alternative hypothesis is also discussed. However, the null distribution of the test statistic contains an unknown tail index. To address this challenge, I present a bootstrap approximation method for determining the rejection region of this test. Simulation studies of artificial data are conducted to assess the finite sample performance, which shows that our method is better than the kernel method in all listed cases. The analysis of real data also demonstrates the excellent performance of this method.

Genetic and phenotypic parameters for 21 wild oat species collected from Jordan and two commercial cultivars (Dolphin and F199084-d4 imported from Australia and Canada, respectively) were investigated. Significant differences were found for seedling emergence, panicle emergence, leaf number per plant, leaf height, head height, fertile tiller number per plant, flag leaf length, flag leaf width, awn length, and seed weight per plant. Genetic parameters and relationships among oat genotypes were established using ten AFLP primer combinations. The ten AFLP primer combinations produced 1157 bands among which 339 were polymorphic, representing 29.3 % polymorphism. These polymorphic bands were sufficient to estimate genetic variation among oat genotypes. The unweighted pair group method with arithmetic average (UPGMA) classified the genotypes into two groups based on Dice similarity coefficient, ranging from a high of 0.78 to a low of 0.26. Clustering based on phenotypical traits showed two main groups that are slightly different compared to AFLP based groups. Correlation between matrices of similarity coefficients based on AFLP and morphological data showed low level of positive correlation (r= 0.26) based on Mantel test. The genotypes with high performance are considered as a valuable genetic resource for oat breeders. [ABSTRACT FROM AUTHOR]

Time series, as frequently the case in neuroscience, are rarely stationary, but often exhibit abrupt changes due to attractor transitions or bifurcations in the dynamical systems producing them. A plethora of methods for detecting such change points in time series statistics have been developed over the years, in addition to test criteria to evaluate their significance. Issues to consider when developing change point analysis methods include computational demands, difficulties arising from either limited amount of data or a large number of covariates, and arriving at statistical tests with sufficient power to detect as many changes as contained in potentially high-dimensional time series. Here, a general method called Paired Adaptive Regressors for Cumulative Sum is developed for detecting multiple change points in the mean of multivariate time series. The method's advantages over alternative approaches are demonstrated through a series of simulation experiments. This is followed by a real data application to neural recordings from rat medial prefrontal cortex during learning. Finally, the method's flexibility to incorporate useful features from state-of-the-art change point detection techniques is discussed, along with potential drawbacks and suggestions to remedy them.

Time series, as frequently the case in neuroscience, are rarely stationary, but often exhibit abrupt changes due to attractor transitions or bifurcations in the dynamical systems producing them. A plethora of methods for detecting such change points in time series statistics have been developed over the years, in addition to test criteria to evaluate their significance. Issues to consider when developing change point analysis methods include computational demands, difficulties arising from either limited amount of data or a large number of covariates, and arriving at statistical tests with sufficient power to detect as many changes as contained in potentially high-dimensional time series. Here, a general method called Paired Adaptive Regressors for Cumulative Sum is developed for detecting multiple change points in the mean of multivariate time series. The method's advantages over alternative approaches are demonstrated through a series of simulation experiments. This is followed by a real data application to neural recordings from rat medial prefrontal cortex during learning. Finally, the method's flexibility to incorporate useful features from state-of-the-art change point detection techniques is discussed, along with potential drawbacks and suggestions to remedy them. [ABSTRACT FROM AUTHOR]

Semi-parametric geographically weighted generalized linear models (S-GWGLMs) are a useful tool in modeling a regression relationship where the impact of certain explanatory variables on a non-Gaussian distributed response variable is global while that of others is spatially varying. In this article, we propose for S-GWGLMs a new estimation method, called two-stage geographically weighted maximum likelihood estimation, and further develop a likelihood ratio statistic-based bootstrap test to determine constant coefficients in the models. The performance of the estimation and test methods is then evaluated by simulations. The results show that the proposed estimation method performs as well as the existing method in estimating both constant and spatially varying coefficients but it is more efficient in terms of computation time; the bootstrap test is of accurate size under the null hypothesis and satisfactory power in identifying spatially varying coefficients. A real-world data set is finally analyzed to demonstrate the application of the proposed estimation and test methods. [ABSTRACT FROM AUTHOR]

This paper tests empirically the causal relationship between bank loans and the monetary base before and after the adoption of inflation targeting in seven Asia-Pacific countries using Toda-Yamamoto Granger non causality test and the bootstrap test for causality. The most striking finding is that the bank loans Granger cause the monetary base during the inflation targeting period in all the countries, except Japan, which was under the influence of the quantitative easing, whereas the causality appeared diverse before the inflation targeting regime. This result implies the need for the policy makers to take the endogenous nature of the money supply into account in the modern economy. [ABSTRACT FROM AUTHOR]

Monte Carlo evaluation of resampling-based tests is often conducted in statistical analysis. However, this procedure is generally computationally intensive. The pooling resampling-based method has been developed to reduce the computational burden but the validity of the method has not been studied before. In this article, we first investigate the asymptotic properties of the pooling resampling-based method and then propose a novel Monte Carlo evaluation procedure namely the n-times pooling resampling-based method. Theorems as well as simulations show that the proposed method can give smaller or comparable root mean squared errors and bias with much less computing time, thus can be strongly recommended especially for evaluating highly computationally intensive hypothesis testing procedures in genetic epidemiology. [ABSTRACT FROM AUTHOR]