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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]

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]

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]

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]

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]

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]

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]

A recursive formula for computing the exact value of score vectors is proposed for a general form of the linear Gaussian state space model, which is more desirable than approximate values in some statistical analyses. Unlike most extant methods, our formula calculates all components of the score vector simultaneously. This approach significantly simplifies its programing, in particular, with some matrix-oriented programing languages, such as MATLAB. We also consider a way of handling initial conditions that depend on unknown parameters. This issue has not yet been explicitly addressed in the existing literature in the context of exact score computing for a general case, such as the one that we consider in this paper. It is also shown that our formula is especially useful for calculating score tests with an outer product of gradient asymptotic covariance matrix estimator. [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]

Measures of association play a central role in the social sciences to quantify the strength of a linear relationship between the variables of interest. In many applications researchers can translate scientific expectations to hypotheses with equality and/or order constraints on these measures of association. In this paper a Bayes factor test is proposed for testing multiple hypotheses with constraints on the measures of association between ordinal and/or continuous variables, possibly after correcting for certain covariates. This test can be used to obtain a direct answer to the research question how much evidence there is in the data for a social science theory relative to competing theories. The stand-alone software package 'BCT' allows users to apply the methodology in an easy manner. The methodology will also be available in the R package 'BFpack'. An empirical application from leisure studies about the associations between life, leisure and relationship satisfaction and an application about the differences about egalitarian justice beliefs across countries are used to illustrate the methodology. [ABSTRACT FROM AUTHOR]

In this paper, we propose several tests for monotonic trend based on the Brillinger's test statistic (1989, Biometrika, 76, 23–30). When there are highly correlated residuals or short record lengths, Brillinger's test procedure tends to have significance level much higher than the nominal level. It is found that this could be related to the discrepancy between the empirical distribution of the test statistic and the asymptotic normal distribution. Hence, in this paper, we propose three bootstrap-based procedures based on the Brillinger's test statistic to test for monotonic trend. The performance of the proposed test procedures is evaluated through an extensive Monte Carlo simulation study, and is compared to other trend test procedures in the literature. It is shown that the proposed bootstrap-based Brillinger test procedures can well control the significance levels and provide satisfactory power performance in testing the monotonic trend under different scenarios. [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]

Multivariate isotonic regression theory plays an important role in the field of testing statistical hypotheses under order restriction for several vector valued parameters and this problem may arise in several situations. Sasabuchi et al. derived a likelihood ratio type test for homogeneity of order restricted mean vectors under a multivariate normality assumption with unknown and common covariance matrices; however, its null distribution depends on the unknown covariance matrix. In order to overcome this difficulty, in the present paper we propose a test that conditions on the sufficient statistic for the covariance matrix. We remove the dependence of the null distribution of test statistic proposed by Sasabuchi et al. on the unknown value of covariance matrix and present a condition on a sufficient statistic for covariance matrix under the null hypothesis. A method based on Markov Chain Monte Carlo sampling is used to calculate the p - value of the test. We evaluate the operating characteristics of the proposed test through simulation studies. Our extensive simulation studies demonstrate that the power of this new test is generally higher than the test proposed by Sasabuchi et al. and also by Sasabuchi. A real data example is presented. [ABSTRACT FROM AUTHOR]

We investigate multiple testing and variable selection using the Least Angle Regression (LARS) algorithm in high dimensions under the assumption of Gaussian noise. LARS is known to produce a piecewise affine solution path with change points referred to as the knots of the LARS path. The key to our results is an expression in closed form of the exact joint law of a |$K$| -tuple of knots conditional on the variables selected by LARS, the so-called post-selection joint law of the LARS knots. Numerical experiments demonstrate the perfect fit of our findings. This paper makes three main contributions. First, we build testing procedures on variables entering the model along the LARS path in the general design case when the noise level can be unknown. These testing procedures are referred to as the Generalized |$t$| -Spacing tests and we prove that they have an exact non-asymptotic level (i.e. the Type I error is exactly controlled). This extends work of [ 31 ] where the spacing test works for consecutive knots and known variance. Second, we introduce a new exact multiple testing procedure after model selection in the general design case when the noise level may be unknown. We prove that this testing procedure has exact non-asymptotic level for general design and unknown noise level. Third, we prove exact control of the false discovery rate under orthogonal design assumption. Monte-Carlo simulations and a real data experiment are provided to illustrate our results in this case. Of independent interest, we introduce an equivalent formulation of the LARS algorithm based on a recursive function. [ABSTRACT FROM AUTHOR]

Missing data are common in medical and social science studies and often face a serious challenge in ultrahigh-dimensional data analysis. In this paper, a nonparametric feature screening approach based on the imputation technique is proposed for ultrahigh-dimensional data with responses missing at random, where the imputation technique is used to replacing each missing value with a set of plausible values. Our approach has many advantages. On one hand, the suggested method relies only on imputation, and its impact is considerably less than that of the feature screening procedure based on the inverse probability weighting approach in missing probabilities. On the other hand, our method does not rely on any model assumption and works generally for all kinds of situations. Simulation studies are conducted to examine the performance of our approach, and a real data example is also presented for illustration. [ABSTRACT FROM AUTHOR]

Efficient and significant empirical estimate of the multivariate regression parameters will be helpful for the policy maker to make the right decisions about sophisticated interrelated issues in the dynamic world. Since the end of the twentieth century, statisticians are going forward to develop unique working methodology for estimating and testing restricted parameters. This paper reviews existing methods and suggests modified maximum likelihood estimator, modified multivariate t statistic and modified joint confidence interval to get efficient estimates for exact linear restricted parameters of multivariate regression with continuous responses. A Monte Carlo experiment is conducted to examine relative performance of the modified methods. The modified methods are found to perform better than the existing methods. [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]

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]

For multi-sample covariance testing, the classical likelihood ratio test is often efficient and powerful in low-dimensional normal cases. However, when the dimension is larger than the sample size, it fails to work in practice and theory. This paper proposes a permutation based test to handle high-dimensional covariance testing problem with more than two samples. Numerical studies show that the test controls type I error rate well for both normal and non-normal data. The power performance is also competitive with existing methods. In addition, a real data example of DNA microarray is analyzed for illustration. [ABSTRACT FROM AUTHOR]

In the literature, most works of the specification tests focus on the problem with one-dimensional response or fixed multidimensional responses. In this paper, we develop a new specification test for the parametric models with non-stationary regressor under multidimensional setup, where the dimension of responses may tend to infinity, which fills a gap in the literature. The theoretical results about the asymptotic properties of the proposed test are studied and the optimal rate of the local departure under the alternative hypothesis is also given which ensures the models underpinning by the null and alternative hypotheses can be differentiated. Some simulation studies are done to evaluate the performance of the proposed test with the finite sample. Besides, a real data example based on the US aggregate consumers' consumption data is employed to illustrate the performance. The results of simulation studies and real data analysis both demonstrate the efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

This paper considers a general Bayesian test for the multi-sample problem. Specifically, for M independent samples, the interest is to determine whether the M samples are generated from the same multivariate population. First, M Dirichlet processes are considered as priors for the true distributions generated the data. Then, the concentration of the distribution of the total distance between the M posterior processes is compared to the concentration of the distribution of the total distance between the M prior processes through the relative belief ratio. The total distance between processes is established based on the energy distance. Various interesting theoretical results of the approach are derived. Several examples covering the high dimensional case are considered to illustrate the approach. [ABSTRACT FROM AUTHOR]

In many existing methods of multiple comparison, one starts with either Fisher's p value or the local fdr. One commonly used p value, defined as the tail probability exceeding the observed test statistic under the null distribution, fails to use information from the distribution under the alternative hypothesis. The targeted region of signals could be wrong when the likelihood ratio is not monotone. The oracle local fdr based approaches could be optimal because they use the probability density functions of the test statistic under both the null and alternative hypotheses. However, the data-driven version could be problematic because of the difficulty and challenge of probability density function estimation. In this paper, we propose a new method, Cdf and Local fdr Assisted multiple Testing method (CLAT), which is optimal for cases when the p value based methods are optimal and for some other cases when p value based methods are not. Additionally, CLAT only relies on the empirical distribution function which quickly converges to the oracle one. Both the simulations and real data analysis demonstrate the superior performance of the CLAT method. Furthermore, the computation is instantaneous based on a novel algorithm and is scalable to large data sets. [ABSTRACT FROM AUTHOR]

Pathway idea is a switching mechanism by which one can go from one functional form to another, and to yet another. In this paper, we introduce a q-Esscher transformed Laplace distribution, which is a stretched model for Esscher transformed Laplace distribution, obtained by introducing a new pathway parameter q, which facilitates a slow transition to the Esscher transformed Laplace distribution as q → 1. This pathway model can be obtained by optimizing Mathai's generalized entropy with more general setup, which is a generalization of various entropy measures due to Shannon and others. The various properties of the q-Esscher transformed Laplace distribution are studied and its applications are discussed. [ABSTRACT FROM AUTHOR]

The testing covariance equality is of importance in many areas of statistical analysis, such as microarray analysis and quality control. Conventional tests for the finite-dimensional covariance do not apply to high-dimensional data in general, and tests for the high-dimensional covariance in the literature usually depend on some special structure of the matrix and whether the dimension diverges. In this paper, we propose a jack-knife empirical likelihood method to test the equality of covariance matrices. The asymptotic distribution of the new test is regardless of the divergent or fixed dimension. Simulation studies show that the new test has a very stable size with respect to the dimension and it is also more powerful than the test proposed by Schott (2007) and studied by Srivastava and Yanagihara (2010). Furthermore, we illustrate the method using a breast cancer dataset. [ABSTRACT FROM AUTHOR]