Search

We propose a general and relatively simple method to construct goodness-of-fit tests on the sphere and the hypersphere. The method is based on the characterization of probability distributions via their characteristic function, and it leads to test criteria that are convenient regarding applications and consistent against arbitrary deviations from the model under test. We emphasize goodness-of-fit tests for spherical distributions due to their importance in applications and the relative scarcity of available methods. [ABSTRACT FROM AUTHOR]

Identifying significant variables for the high-dimensional logistic regression model is a fundamental problem in modern statistics and machine learning. This paper introduces a stability knockoffs (StabKoff) selection procedure by merging stability selection and knockoffs to conduct controlled variable selection for logistic regression. Under some regularity conditions, we show that the proposed method achieves FDR control under the finite-sample setting, and the power also asymptotically approaches one as the sample size tends to infinity. In addition, we further develop an intersection strategy that allows better separation of knockoff statistics between significant and unimportant variables, which in some cases leads to an increase in power. The simulation studies demonstrate that the proposed method possesses satisfactory finite-sample performance compared with existing methods in terms of both FDR and power. We also apply the proposed method to a real data set on opioid use disorder treatment. [ABSTRACT FROM AUTHOR]

The concept of antidistinguishability of quantum states has been studied to investigate foundational questions in quantum mechanics. It is also called quantum state elimination, because the goal of such a protocol is to guess which state, among finitely many chosen at random, the system is not prepared in (that is, it can be thought of as the first step in a process of elimination). Antidistinguishability has been used to investigate the reality of quantum states, ruling out ψ -epistemic ontological models of quantum mechanics (Pusey et al. in Nat Phys 8(6):475–478, 2012). Thus, due to the established importance of antidistinguishability in quantum mechanics, exploring it further is warranted. In this paper, we provide a comprehensive study of the optimal error exponent—the rate at which the optimal error probability vanishes to zero asymptotically—for classical and quantum antidistinguishability. We derive an exact expression for the optimal error exponent in the classical case and show that it is given by the multivariate classical Chernoff divergence. Our work thus provides this divergence with a meaningful operational interpretation as the optimal error exponent for antidistinguishing a set of probability measures. For the quantum case, we provide several bounds on the optimal error exponent: a lower bound given by the best pairwise Chernoff divergence of the states, a single-letter semi-definite programming upper bound, and lower and upper bounds in terms of minimal and maximal multivariate quantum Chernoff divergences. It remains an open problem to obtain an explicit expression for the optimal error exponent for quantum antidistinguishability. [ABSTRACT FROM AUTHOR]

We address the problem of testing for the invariance of a probability measure under the action of a group of linear transformations. We propose a procedure based on consideration of one-dimensional projections, justified using a variant of the Cramér–Wold theorem. Our test procedure is powerful, computationally efficient, and dimension-independent, extending even to the case of infinite-dimensional spaces (multivariate functional data). It includes, as special cases, tests for exchangeability and sign-invariant exchangeability. We compare our procedure with some previous proposals in these cases, in a small simulation study. The paper concludes with two real-data examples. [ABSTRACT FROM AUTHOR]

Correlation matrices are an essential tool for investigating the dependency structures of random vectors or comparing them. We introduce an approach for testing a variety of null hypotheses that can be formulated based upon the correlation matrix. Examples cover MANOVA-type hypothesis of equal correlation matrices as well as testing for special correlation structures such as sphericity. Apart from existing fourth moments, our approach requires no other assumptions, allowing applications in various settings. To improve the small sample performance, a bootstrap technique is proposed and theoretically justified. Based on this, we also present a procedure to simultaneously test the hypotheses of equal correlation and equal covariance matrices. The performance of all new test statistics is compared with existing procedures through extensive simulations. [ABSTRACT FROM AUTHOR]

We consider goodness-of-fit methods for multivariate symmetric and asymmetric stable Paretian random vectors in arbitrary dimension. The methods are based on the empirical characteristic function and are implemented both in the i.i.d. context as well as for innovations in GARCH models. Asymptotic properties of the proposed procedures are discussed, while the finite-sample properties are illustrated by means of an extensive Monte Carlo study. The procedures are also applied to real data from the financial markets. [ABSTRACT FROM AUTHOR]

We consider the problem of detecting distributional changes in a sequence of high dimensional data. Our approach combines two separate statistics stemming from L p norms whose behavior is similar under H 0 but potentially different under H A , leading to a testing procedure that that is flexible against a variety of alternatives. We establish the asymptotic distribution of our proposed test statistics separately in cases of weakly dependent and strongly dependent coordinates as min { N , d } → ∞ , where N denotes sample size and d is the dimension, and establish consistency of testing and estimation procedures in high dimensions under one-change alternative settings. Computational studies in single and multiple change point scenarios demonstrate our method can outperform other nonparametric approaches in the literature for certain alternatives in high dimensions. We illustrate our approach through an application to Twitter data concerning the mentions of U.S. governors. [ABSTRACT FROM AUTHOR]

Exponential random graph models (ERGM) are known as one of the most flexible models for profile monitoring of the complex structure of dynamic social networks, especially for networks with a large number of nodes. Usually, only one realization of a network is available instead of a random sample and the correlations between nodes increase the computational cost. Parametrizing via ERGM, the parameters of the model corresponding to the features of the network (namely, edges, k -star, and triangles) are then monitored using Hotelling's T 2 and likelihood ratio test control charts in Phase I for two general scenarios in both the directed and undirected edges cases. The results show that the presented control charts efficiently characterize the profile consisting of a network at each sampling time. The power of each method at a constant nominal Type I error probability is numerically reported for different shifts in the parameters. The results are also employed in the analysis of Gnutella Internet Peer-to-Peer Networks. [ABSTRACT FROM AUTHOR]

This article examines the influence of the life context on entrepreneurial passion (EP) and performance. Drawing on the person–environment fit theory, we developed a model showing how the life context fit affects EP in the domains of founding, inventing, and developing and how this translates into performance. Using partial least squares structural equation modeling, we tested our hypotheses using a sample of 406 entrepreneurs from the cultural and creative industries. Due to the presence of unobserved heterogeneity in the overall sample, we conducted prediction-oriented segmentation, which revealed four segments in which life contexts exhibit not only positive but also adverse effects on EP. Our results showed that, in contrast to the extant literature, EP generates its overall effect on performance at the intersection of positively and negatively acting domains of founding, inventing, and developing. To explain the structural relations between the four segments, we conducted qualitative post hoc analyses to evaluate idiosyncratic data on passion and the life context and aligned our insights with the extant literature delineating four categories of entrepreneurs from the cultural and creative industries: artepreneurs, culturepreneurs, creative entrepreneurs, and lifestyle entrepreneurs. Our findings contribute to overcoming the dichotomy between passion as a personality trait and a dynamic construct and to understanding passion as an individual phenomenon with multiple sources that interacts with the proximal environment and that can impact entrepreneurial performance both positively and negatively. We extend the entrepreneurship and psychology literature, facilitating people's abilities to lead more entrepreneurial and passionate lives. [ABSTRACT FROM AUTHOR]

This paper focuses on the likelihood ratio test (LRT) statistics for different hypothesis tests. Assuming that a random sample is from a normal population, we make the sample size n and the dimension p close to infinity and satisfy p < n − c for some 1 ≤ c ≤ 4. Based on this assumption, the moderate deviation principle (MDP) for the LRT will be given under the null hypothesis. The corresponding numerical simulation results are shown at the end of the paper. [ABSTRACT FROM AUTHOR]

We consider an exchangeably weighted bootstrap for function-valued estimators defined as a zero point of a function-valued random criterion function. A large number of bootstrap resampling schemes emerge as special cases of our settings. The main ingredient is the use of a differential identity that applies when the random criterion function is linear in terms of the empirical measure. Our results are general and do not require linearity of the statistical model in terms of the unknown parameter. We also consider the semiparametric models extending Zhan's work to a more delicate framework. The theoretical results established in this paper are (or will be) key tools for further developments in the parametric and semiparametric models. [ABSTRACT FROM AUTHOR]

Handling large datasets and calculating complex statistics on huge datasets require important computing resources. Using subsampling methods to calculate statistics of interest on small samples is often used in practice to reduce computational complexity, for instance using the divide and conquer strategy. In this article, we recall some results on subsampling distributions and derive a precise rate of convergence for these quantities and the corresponding quantiles. We also develop some standardisation techniques based on subsampling unstandardised statistics in the framework of large datasets. It is argued that using several subsampling distributions with different subsampling sizes brings a lot of information on the behaviour of statistical learning procedures: subsampling allows to estimate the rate of convergence of different algorithms, to estimate the variability of complex statistics, to estimate confidence intervals for out-of-sample errors and interpolate their values at larger scales. These results are illustrated on simulations, but also on two important datasets, frequently analysed in the statistical learning community, EMNIST (recognition of digits) and VeReMi (analysis of Network Vehicular Reference Misbehavior). [ABSTRACT FROM AUTHOR]

Several tests for simultaneously checking homogeneity of means and covariance matrices, based on the classical likelihood ratio and sum-of-squares type test statistics, have been proposed in the literature. Despite their usefulness, they tend to have unsatisfactory size performance for either nonnormal high-dimensional data or strongly spiked eigenvalue models. This article proposes a novel high-dimensional nonparametric test using the maximum-type statistics. The limiting null distribution of the proposed test statistic is derived to provide p-values. It turns out that under appropriate conditions, the test is particularly powerful against sparse alternatives. Since the unknown correlations among the data sometimes pose a great challenge toward the accurate p-value calculation of the test, we further use a parametric bootstrap technique to achieve size accuracy. We also prove that the proposed simulation-based testing procedure is asymptotically valid in terms of size and power even if the dimension of the data is much larger than the sample size. We demonstrate the effectiveness of our methods through extensive simulation studies and a real data application. [ABSTRACT FROM AUTHOR]

AbstractA notion of data depth is used to measure the centrality/outlyingness of a given point with respect to a given distribution or data cloud. Several depth-based graphical tools and nonparametric tests have been proposed for the comparison of two or more multivariate distributions. This article proposes graphical tools for the comparison of multiple multivariate distributions. These graphical tools can be considered as a generalization of the well-known depth versus depth plot (DD-plot) for the visual comparison of the two multivariate samples. Different types of variations in location, scale, skewness, or kurtosis among distributions give rise to different deviation patterns in the proposed plots. Based on these graphical tools we generalize the statistical tests for two sample location, scale, and homogeneity of species assemblages to the corresponding multi-sample problems. An extensive simulation study reveals that the performances of the proposed tests are superior to that of the existing tests. The proposed graphical tools and tests are illustrated with different simulated and real data sets. [ABSTRACT FROM AUTHOR]

Independence test based on empirical copula does not perform well in the presence of weak dependency or when dependency occurs only in the tails. The copula density, which is estimated by a local likelihood probit transformation method, is used to detect the independence. In this article, three nonparametric tests of independence based on α-divergence and copula density are introduced. These tests are capable of considering weak dependency. The asymptotic consistency of the copula-based α-divergence estimator is also derived. In addition, the empirical powers of the proposed tests are computed through extensive simulations. The results show that the new tests outperform in small sample sizes or weak dependencies. Finally, an application in uranium exploration is presented to illustrate the applicability of the proposed tests. [ABSTRACT FROM AUTHOR]

The fear and uncertainty created by the ongoing COVID-19 pandemic have strained the employer-employee relationship. This research seeks to understand how psychological contract breach shapes employees' perspectives of meaningful work and its influence on turnover intention. Drawing on event systems theory, we investigated how objective events in the environment (e.g., global pandemic) impact employees. We also argue that the career shock caused by the COVID-19 pandemic affected employees' choices based on their job fit and psychological resources. Findings indicate that experiencing meaningful work reduced turnover intention, especially for those that experienced less psychological contract breach. Also, experiencing meaningful work reduced turnover intention most for individuals whose working hours were not impacted by the COVID-19 pandemic. These findings show that experiencing meaningful work in a relatively stable job reduces employees' potential turnover during exogenous shocks. The study also highlights the importance of meaningful work and why organizations should collaborate and assist their employees in making work more meaningful. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new test for linear hypothesis of k-sample mean vectors in high-dimensional normal models based on generalized likelihood ratio method. The proposed test is designed for the "large p small n" situation where the data dimension p is much larger than the sample size n. The asymptotic null and non null distributions of the proposed test are derived under mild conditions. Simulation results show that our new test outperforms some competitors in both size and power. Moreover, our new test can also be applied to non normal data. [ABSTRACT FROM AUTHOR]

We consider the nonparametric classification of high dimensional, low sample size (HDLSS) data where the classical discrimination methods break down due to the singularity of the sample covariance matrix. We present new dissimilarity indices, discuss their asymptotic properties in the HDLSS setting, use them in building powerful classifiers, and compare their behavior with existing methods. We illustrate the difficulties with the Euclidean nearest neighbor method and prove that dissimilarity-based classifiers produce misclassification rates that tend to zero as p → ∞ . We present test-based classifiers in the HDLSS setting. A simulation study compares the misclassification rates of diagonal linear discriminant analysis with twelve other nonparametric classifiers. The methods are applied to microarray data for classification of prostate cancer. [ABSTRACT FROM AUTHOR]

This paper proposes a normalizing transformation of the Dempster statistic for testing the equality of two mean vectors with unequal covariance matrices in high-dimensional settings. The distribution of the Dempster statistic is known to converge to a normal distribution as dimension p goes to infinity; however, its rate of convergence is not guaranteed. Therefore, normal approximation is often too loose for medium p settings or fails to capture the tail behavior of the resulting distribution. We developed a concept of normalizing transformation of a statistic based on the rate of convergence to normality and show that the rate of convergence to normality is improved by normalizing transformation of the Dempster statistic. [ABSTRACT FROM AUTHOR]

Abstract When there are some restrictions on the random variables of insurance risk model, it is impossible to calculate the exact value of ruin probabilities. For these cases, even finding a suitable approximation, is very important from a practical point of view. In the present paper, we consider the renewal insurance surplus model with light-tailed claim amount distributions and try to find some inequalities on the infinite time ruin probability depending on the amount of initial reserve using statistical and mathematical approaches if the assumption of net profit does not hold but there exist some other restrictions on the mathematical functions of random variables of model. The assertions depend on the amount of initial reserve, distribution of nonnegative claim occurrences times and successive claim amounts are obtained. Finally, to show the application and effectiveness of given theorems two examples are presented. Through these examples, the infinite time ruin probabilities are estimated using Monte Carlo simulation and give an intuitive way to understand the nature of ruin. [ABSTRACT FROM AUTHOR]

We employ a general Monte Carlo method to test composite hypotheses of goodness-of-fit for several popular multivariate models that can accommodate both asymmetry and heavy tails. Specifically, we consider weighted L2-type tests based on a discrepancy measure involving the distance between empirical characteristic functions and thus avoid the need for employing corresponding population quantities which may be unknown or complicated to work with. The only requirements of our tests are that we should be able to draw samples from the distribution under test and possess a reasonable method of estimation of the unknown distributional parameters. Monte Carlo studies are conducted to investigate the performance of the test criteria in finite samples for several families of skewed distributions. Real-data examples are also included to illustrate our method. [ABSTRACT FROM AUTHOR]

Food safety governance and its performance improvement are inseparable from the cooperation of multiple subjects. The extensive participation of consumers is not only a powerful supplement to the deficiency of supervision resources but also an intrinsic request for seeking both temporary and permanent solutions to food safety problems. To ascertain the factors and mechanisms that influence consumers’ participation willingness of food safety social co-governance, an integrated conceptual model was constructed based on the Theory of Planned Behavior (TPB) and Benefit-Risk Analysis (BRA). 664 valid questionnaires collected in China were used to verify the hypotheses with the application of the Structural Equation Model (SEM). The results showed that behavior attitude, subjective norm, and perceived behavior control significantly impacted consumers’ participation willingness, and the influence strength was in the sequence of perceived behavior control, behavior attitude, and subjective norm. Perceived risk and participation willingness showed a strong negative relationship. Besides that, perceived risk also played a partial mediation role in predicting behavior attitudes toward consumers’ participation willingness. According to the conclusions, a series of policy suggestions were also proposed to help improve consumers’ participation willingness.

The authors considered the problem of testing equality of two multivariate normal mean vectors when the covariance matrices are unknown and arbitrary. Given auxiliary variables with known means, the authors proposed a pivotal quantity which is similar to the Hotelling T2 statistic and obtained a satisfying approximation to its distribution. The authors also outlined hypothesis testing and confidence estimation based on the approximate distribution. The merits of the test were studied using Monte Carlo simulation. Monte Carlo studies indicated that the test is very satisfactory even for moderately small samples. At last, the authors illustrated the proposed methods by an example. [ABSTRACT FROM AUTHOR]

Matrix-valued data arise in many applications. In this paper, we consider the setting where one collects both a matrix-valued data Y ∈ R p × q and a generic scalar X that can be continuous, discrete or categorical. Since the rows and columns of Y often have specific meanings in practice, it is interesting to make statistical inferences on the significance of rows and columns of Y . In this paper, by taking into account the background effect, we propose a new measure on significance of rows and columns based on an additive model. The point estimates, hypothesis testings and confidence intervals of the significance of a given row or column of Y are considered. Moreover, a procedure is proposed to select significant rows and columns. Our method is applicable to both p and q being much larger than sample size n. Simulation results and real data analysis demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The precise modeling of bivariate continuous characteristics remains an actual challenge in probability and statistics. In this paper, we explore a new strategy based on the combination of a simple polynomial-sine copula and the Burr XII distribution. The idea is to use the oscillating functionalities of the polynomial-sine copula and the flexibility of the Burr XII distribution to propose a serious bivariate solution for the modelling of bivariate lifetime phenomena. Both theory and practice are developed. In particular, we determine the main functions related to the distribution, like the cumulative distribution function, probability density function, conditional density function, and hazard rate function, and perform a moment analysis, including various useful measures for bivariate modeling. On the practical plan, we derive the maximum likelihood and Bayes estimates for the unknown parameters. Also, the bootstrap confidence interval and the highest posterior density interval are obtained. The performance of the proposed bivariate distributions is examined using a simulation study. Finally, one data set is considered to illustrate its flexibility for real-life applications. [ABSTRACT FROM AUTHOR]

Bibliometric measurements are becoming omnipresent and crucially important for academic career decisions. The measured criteria induce strong incentives to align academics' time and efforts. Based on a survey of economics and business scholars in German-speaking countries, this article empirically explores the interactions between scientific journal metrics and the behavior of authors in the publishing process. The impact different types of pressure have on their decisions is emphasized. In line with rational choice, authors generally move down in journal ranking as they resubmit their papers. While the process is highly influenced by random elements, the Scimago journal rank provides the best fit to researcher's behavior. Doctoral students initially submit to lower ranked journals compared to higher academic positions, which is likely due to the time pressure they face. The empirical findings improve our understanding of strategic responses in the scientific publishing process. Qualified lotteries, along with other propositions, are suggested to mitigate adverse responses by academics. [ABSTRACT FROM AUTHOR]

In a pairwise exchangeable dependence setting for test statistics, the cutoffs of Sarkar et al. (2016) may be viewed as a first iteration improvement of Holm (1979)’s classical cutoffs under a convexity condition on the copula. The cutoffs of Seneta and Chen (1997) which improve Holm’s in the present exchangeability setting, are shown, after an analogous first iteration step, to lead to a refinement of Sarkar et al. (2016). Further, we show that the convexity condition can be circumvented in practice, computationally. Improvement by iteration limit of cutoffs is considered for both procedures. Comparisons between the effects of the several cutoff sets are made by way of plots of the familywise error rate against correlation ρ in the classic setting of the multivariate Normal; and the distributional setting of the multivariate Generalized Hyperbolic for the important Variance Gamma type subfamily, for which a convexity condition cannot be analytically verified. [ABSTRACT FROM AUTHOR]

Consider the likelihood ratio test (LRT) statistics for the independence of sub-vectors from a p-variate normal random vector. We are devoted to deriving the limiting distributions of the LRT statistics based on a random sample of size n. It is well known that the limit is chi-square distribution when the dimension of the data or the number of the parameters are fixed. In a recent work by Qi et al. (Ann Inst Stat Math 71:911–946, 2019), it was shown that the LRT statistics are asymptotically normal under condition that the lengths of the normal random sub-vectors are relatively balanced if the dimension p goes to infinity with the sample size n. In this paper, we investigate the limiting distributions of the LRT statistic under general conditions. We find out all types of limiting distributions and obtain the necessary and sufficient conditions for the LRT statistic to converge to a normal distribution when p goes to infinity. We also investigate the limiting distribution of the adjusted LRT test statistic proposed in Qi et al. (2019). Moreover, we present simulation results to compare the performance of classical chi-square approximation, normal and non-normal approximation to the LRT statistics, chi-square approximation to the adjusted test statistic, and some other test statistics. [ABSTRACT FROM AUTHOR]

In this paper we study the change point detection for the mean of multivariate time series. We construct the weighted U-statistic change point tests based on the weight function 1 / t (1 - t) and some suitable kernel functions. We establish the asymptotic distribution of the test statistic under the null hypothesis and the consistency under the alternatives. A bootstrap procedure is applied to approximate the distribution of the test statistic and it is proved that the test statistic based on bootstrap sampling has the same asymptotic distribution as the original statistic. Numerical simulation and real data analysis show the good performance of the weighted change point test especially when the change point location is not in the middle of the observation period. [ABSTRACT FROM AUTHOR]

It is common in a number of disciplines such as economics, sociology, psychology and clinical trials that researchers are interested to test treatment effects among several of the outcomes in the same direction. Such tests can be performed by using equi-directional test statistics for multivariate data. If on the other hand, treatment effects with respect to one or more of the outcomes differ in direction, the power of equi-directional tests is compromised. Thus, we interchanged the signs of different outcomes by multiplying the values with - 1 and made the anticipated direction similar. Following this, we employed a recently proposed test statistic which handles equi-directional alternatives since the direction of treatment effects is made uniform through interchanging the signs. Once monotonic trend, that is, monotonic increasing for some of the outcomes and monotonic decreasing for others is demonstrated through the global test statistic, an investigator may be further interested in which specific outcomes or sets of outcomes actually these trends are observed. To address this issue, we adapted a closed testing principle. The whole procedure is illustrated by data sets from a toxicology study carried out by the National Toxicology Program, and a cost of transporting milk from farms to dairy plants per mile by different trucks. [ABSTRACT FROM AUTHOR]

The sandwiched Rényi α -divergences of two finite-dimensional quantum states play a distinguished role among the many quantum versions of Rényi divergences as the tight quantifiers of the trade-off between the two error probabilities in the strong converse domain of state discrimination. In this paper, we show the same for the sandwiched Rényi divergences of two normal states on an injective von Neumann algebra, thereby establishing the operational significance of these quantities. Moreover, we show that in this setting, again similarly to the finite-dimensional case, the sandwiched Rényi divergences coincide with the regularized measured Rényi divergences, another distinctive feature of the former quantities. Our main tool is an approximation theorem (martingale convergence) for the sandwiched Rényi divergences, which may be used for the extension of various further results from the finite-dimensional to the von Neumann algebra setting. We also initiate the study of the sandwiched Rényi divergences of pairs of states on a C ∗ -algebra and show that the above operational interpretation, as well as the equality to the regularized measured Rényi divergence, holds more generally for pairs of states on a nuclear C ∗ -algebra. [ABSTRACT FROM AUTHOR]

The difficulty to efficiently compute the null distribution of the largest eigenvalue of a MANOVA matrix has hindered the wider applicability of Roy's Largest Root Test (RLRT) though it was proposed over six decades ago. Recent progress made by Johnstone, Butler and Paige and Chiani has greatly simplified the approximate and exact computation of the critical values of RLRT. When datasets are high dimensional (HD), Chiani's numerical algorithm of exact computation may not give reliable results due to truncation error, and Johnstone's approximation method via Tracy-Widom distribution is likely to give a good approximation. In this paper, we conduct comparative studies to study in which region the exact method gives reliable numerical values, and in which region Johnstone's method gives a good quality approximation. We formulate recommendations to inform practitioners of RLRT. We also conduct simulation studies in the high dimensional setting to examine the robustness of RLRT against normality assumption in populations. Our study provides support of RLRT robustness against non-normality in HD. [ABSTRACT FROM AUTHOR]

With the rapid development of modern computing techniques, high-dimensional data are increasingly encountered in many studies. In this paper, we propose a three-step method to study the mean testing problem. The proposed test is based on the p-values calculated from the univariate tests and the dimension reduction method. Since it does not require explicit conditions of data dimension and sample size, we can use it to solve the mean testing problem of high-dimensional data, especially when the data dimension is much larger than the sample size. The new method can be implemented for the normal and non-normal distribution, which has a wide application. Various simulations are conducted to compare the testing power of the new method and the existing tests. The comparison shows that the new method has higher testing power. We also apply the proposed method to a real example of gene expression data. [ABSTRACT FROM AUTHOR]

We propose a Bayesian hypothesis testing procedure for comparing the multivariate distributions of several treatment groups against a control group. This test is derived from a flexible model for the group distributions based on a random binary vector such that, if its jth element equals one, then the jth treatment group is merged with the control group. The group distributions’ flexibility comes from a dependent Dirichlet process, while the latent vector prior distribution ensures a multiplicity correction to the testing procedure. We explore the posterior consistency of the Bayes factor and provide a Monte Carlo simulation study comparing the performance of our procedure with state-of-the-art alternatives. Our results show that the presented method performs better than competing approaches. Finally, we apply our proposal to two classical experiments. The first one studies the effects of tuberculosis vaccines on multiple health outcomes for rabbits, and the second one analyzes the effects of two drugs on weight gain for rats. In both applications, we find relevant differences between the control group and at least one treatment group. [ABSTRACT FROM AUTHOR]