Tests of Multivariate Independence for Ordinal Data

Population and sample versions of Kendall and Spearman measures of association suitable for multivariate ordinal data are defined. The latter generalize the indices of dependence of Ruymgaart and van Zuijlen (1978 Ruymgaart , F. H. , van Zuijlen , M. C. A. ( 1978 ). Asymptotic normality of multivariate linear rank statistics in the non-i.i.d. case . Ann. Statist. 6 : 588 – 602 .[Crossref], [Web of Science ®] , [Google Scholar]), Joe (1990 Joe , H. ( 1990 ). Multivariate concordance . J. Multivariate Anal. 35 : 12 – 30 .[Crossref], [Web of Science ®] , [Google Scholar]), and Schmid and Schmidt (2007 Schmid , F. , Schmidt , R. ( 2007 ). Multivariate extensions of Spearman's rho and related statistics . Statist. Probab. Lett. 77 : 407 – 416 .[Crossref], [Web of Science ®] , [Google Scholar]) by allowing atoms in the underlying distribution. The representation of the proposed empirical measures as U-statistics enables to establish their asymptotic normality under general distributions. A special attention is given to tests of independence for multivariate ordinal data, where the power of the new methodologies are investigated under fixed and contiguous alternatives.     

Retrospective Parametric Tests for Homogeneity of Data

The problem considered in this study is that of detecting a change in the unknown parameters of known distribution on the basis of a finite sequence of independent observations, assuming that if a change in the parameters of a distribution has occurred then it is unique. We examine various methods that have been suggested for this problem and suggest a uniform approach, based on likelihood ratio analysis. For tests derived this way we present approximations for levels of significance based on asymptotic analyses. The suggested tests meet the needs of specific problems (such as a one-sided alternative), for which general parametric case solutions have not been suggested explicitly before. We also find that rate of convergence of our asymptotics is fast, and provide accurate results for a level of significance of the suggested tests for sample sizes commonly observed in practice.     

A Power Analysis of Tests for Paired Lifetime Data

A power analysis is used to compare tests developed for a new lifetime model applicable for certain paired-data experiments. Two pivotal quantities are available for exact parametric testing of the equality of the marginal survival distributions. Here, the power of each test will be calculated to find the more powerful test. The analysis leads to an interesting scenario where the two power curves are quite similar and less powerful test may actually be preferred for pragmatic considerations.     

面板数据模型的设定、统计检验和新进展

在介绍面板数据及其优势与局限的基础上,首先,从异质性、时变性和相关性的观点对静态面板数据计量模型的设定、动态面板数据模型的估计方法和Granger因果检验进行系统的讨论。其次,按照假设检验的零假设进行分类,系统阐述面板单位根检验和协整检验理论。最后,介绍面板数据计量经济学的一些新进展。     

A Class of Two-Sample Nonparametric Tests for Panel Count Data

Panel count data frequently occur in many situations including medical follow-up studies and reliability experiments. For two-sample comparison based on panel count data, several procedures have been proposed including Thall and Lachin (1988 Thall , P. F. , Lachin , J. M. ( 1988 ). Analysis of recurrent events: nonparametric methods for random-interval count data . J. Amer. Statist. Assoc. 83 : 339 – 347 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Sun and Fang (2003 Sun , J. , Fang , H. B. ( 2003 ). A nonparametric test for panel count data . Biometrika 90 : 199 – 208 .[Crossref], [Web of Science ®] , [Google Scholar]). In this article, a new class of nonparametric test procedures are presented. The test is a generalization of that for the same problem for failure time data and overcomes some shortcomings of the existing methods. Monte Carlo simulation studies are conducted to evaluate the presented approach and suggest that it works well. An illustrative example is discussed.     

An Empirical Analysis of Some Nonparametric Goodness-of-Fit Tests for Censored Data

In this article, we consider some nonparametric goodness-of-fit tests for right censored samples, viz., the modified Kolmogorov, Cramer–von Mises–Smirnov, Anderson–Darling, and Nikulin–Rao–Robson χ² tests. We also consider an approach based on a transformation of the original censored sample to a complete one and the subsequent application of classical goodness-of-fit tests to the pseudo-complete sample. We then compare these tests in terms of power in the case of Type II censored data along with the power of the Neyman–Pearson test, and draw some conclusions. Finally, we present an illustrative example.     

Weighted Mean Survival Test Statistics: a Class of Distance Tests for Censored Survival Data

A class of test statistics is introduced which is sensitive against the alternative of stochastic ordering in the two-sample censored data problem. The test statistics for evaluating a cumulative weighted difference in survival distributions are developed while taking into account the imbalances in base-line covariates between two groups. This procedure can be used to test the null hypothesis of no treatment effect, especially when base-line hazards cross and prognostic covariates need to be adjusted. The statistics are semiparametric, not rank based, and can be written as integrated weighted differences in estimated survival functions, where these survival estimates are adjusted for covariate imbalances. The asymptotic distribution theory of the tests is developed, yielding test procedures that are shown to be consistent under a fixed alternative. The choice of weight function is discussed and relies on stability and interpretability considerations. An example taken from a clinical trial for acquired immune deficiency syndrome is presented.     

Tests of Covariance Matrices for High Dimensional Multivariate Data Under Non Normality

Ahmad and von Rosen (2014 Ahmad, M. R. (2014). A U-statistic approach for a high-dimensional two-sample mean testing problem under non-normality and Behrens-Fisher setting. Annals of the Institute of Statistical Mathematics 66:33–61.[Crossref], [Web of Science ®] , [Google Scholar]) presented test statistics for sphericity and identity of the covariance matrix of a multivariate normal distribution when the dimension, p, exceeds the sample size, n. In this note, we show that their statistics are robust to normality assumption, when normality is replaced with certain mild assumptions on the traces of the covariance matrix. Under such assumptions, the test statistics are shown to follow the same asymptotic normal distribution as under normality for large p, also when p > >n. The asymptotic normality is proved using the theory of U-statistics, and is based on very general conditions, particularly avoiding any relationship between n and p.     

The Effect of a Variable Data Point on Hypothesis Tests for Means

The effect of a single variable data point, x, on the usual test statistics for traditional hypothesis tests for means is analyzed. It is shown that an outlier may have a profound and unexpected effect on the test statistic. Although it might appear that an outlier would tend to lend support to the alternate hypothesis, it may in fact detract from the significance of the test. In one-population tests and analysis of variance (ANOVA), the value of x that maximizes the significance of the test statistic is given. This value does not have to be unusually large or small. In fact, it often falls within the range of the other sample points. In the general one-population case, the limiting value for the test statistic is shown to be +1. In the case involving more than one population, it is shown that the limiting value of the test statistic is a function only of the number of members in the samples and not their relative values. Special cases are identified in which the test statistic is shown to have unique characteristics depending on the characteristics of the data.     

Non-parametric Tests for the Comparison of Point Processes Based on Incomplete Data

We consider the comparison of point processes in a discrete observation situation in which each subject is observed only at discrete time points and no history information between observation times is available. A class of non-parametric test statistics for the comparison of point processes based on this kind of data is presented and their asymptotic distributions are derived. The proposed tests are generalizations of the corresponding tests for continuous observations. Some results from a simulation study for evaluating the proposed tests are presented and an illustrative example from a clinical trial is discussed.     

Detecting Randomness: A Review of Existing Tests with New Comparisons

In this article we review and compare a number of existing tests for detecting randomness in time series data, with emphasis on stock market index data. By comparing variance ratio tests with traditional statistical tests, we have the most extensive simulation comparison of such procedures. The investigated tests are compared over a diverse group of distributions, models, and stock market applications. In our stock market data analysis, the choice of data transformation can have a noticeable effect on test results. This study provides the reader with a guide as to which test and transformation is most appropriate for their use.     

A Comparative Study of Model-based Tests of Independence for Ordinal Data Using the Bootstrap

When analyzing categorical data using loglinear models in sparse contingency tables, asymptotic results may fail. In this paper the empirical properties of three commonly used asymptotic tests of independence, based on the uniform association model for ordinal data, are investigated by means of Monte Carlo simulation. Five different bootstrapped tests of independence are presented and compared to the asymptotic tests. The comparisons are made with respect to both size and power properties of the tests. Results indicate that the asymptotic tests have poor size control. The test based on the estimated association parameter is severely conservative and the two chi-squared tests (Pearson, likelihood-ratio) are both liberal. The bootstrap tests that either use a parametric assumption or are based on non-pivotal test statistics do not perform better than the asymptotic tests in all situations. The bootstrap tests that are based on approximately pivotal statistics provide both adjustment of size and enhancement of power. These tests are therefore recommended for use in situations similar to those included in the simulation study.     

Simple Tests for Exogeneity of a Binary Explanatory Variable in Count Data Regression Models

This article investigates power and size of some tests for exogeneity of a binary explanatory variable in count models by conducting extensive Monte Carlo simulations. The tests under consideration are Hausman contrast tests as well as univariate Wald tests, including a new test of notably easy implementation. Performance of the tests is explored under misspecification of the underlying model and under different conditions regarding the instruments. The results indicate that often the tests that are simpler to estimate outperform tests that are more demanding. This is especially the case for the new test.     

书面调查和网络调查的区别——两种数据收集方法的比较

随着网络调查的兴起,研究者必须确认网络调查与传统的纸笔调查效果是否相同。从数据收集质量和测量效果两个方面对纸笔调查与网络调查进行了比较。研究发现：纸笔调查与网络调查的测量模型和测量信度没有显著差异,但是,纸笔调查的测量均值高于网络调查,而网络调查的测量误差高于纸笔调查,网络调查的缺失率更低,纸笔调查与网络调查具有测量不变性。     

Green,Yellow, Red: Implementing a New Workflow for Collection Management at College of Charleston Libraries

Abstract

This article summarizes the North Carolina Serials Conference presentation “Green, Yellow, Red: Implementing a New Workflow for Collection Management at College of Charleston Libraries,” a case study evaluating a new workflow created by the Collection and Content Services Department at the College of Charleston Libraries to streamline the renewals process after significant departmental and staff changes. The librarians involved in creating the workflow found that they could not only use it to organize their renewals processes, but also to open communication with faculty regarding underutilized resources.     

A Class of Permutation Tests for the Equality of Two Marginal Survival Functions Using Paired Censored Data

We studied several test statistics for testing the equality of marginal survival functions of paired censored data. The null distribution of the test statistics was approximated by permutation. These tests do not require explicit modeling or estimation of the within-pair correlation, accommodate both paired data and singletons, and the computation is straightforward with most statistical software. Numerical studies showed that these tests have competitive size and power performance. One test statistic has higher power than previously published test statistics when the two survival functions under comparison cross. We illustrate use of these tests in a propensity score matched dataset.     

Locally Most Powerful Rank Tests for Comparison of Two Failure Rates Based on Multiple Type-II Censored Data

This article deals with the locally most powerful rank tests for testing the hypothesis that two failure rates are equal against the alternative that one failure rate is greater than the other, when the combined ordered sample is multiple Type-II censored. A modified version of the Dupa? and Hájek (1969 Dupa? , V. , Hájek , J. (1969). Asymptotic normality of simple linear rank statistics under alternatives II. Ann. Math. Statist. 40:1992–2017.[Crossref] , [Google Scholar]) theorem is used to establish their asymptotic normality under fixed alternative since the scores generating functions associated with these rank test statistics have a finite number of jump discontinuities. The modified version that leads to a simpler centering constant, is proved by Dupa? (1970 Dupa? , V. ( 1970 ). A contribution to the asymptotic normality of simple linear rank statistics. In: Puri, M. L., ed. Nonparametric Techniques in Statistical Inference. Cambridge: Cambridge University Press . [Google Scholar]) using the results of Hájek (1968 Hájek , J. ( 1968 ). Asymptotic normality of simple linear rank statistics under alternatives . Ann. Math. Statist. 39 : 325 – 346 .[Crossref] , [Google Scholar]). The Pitman AREs of these rank tests based on censored data relative to the corresponding tests based on complete data are obtained under some Lehmann-type alternative distributions such that their failure rates dominate the failure rates of the respective null distributions. The AREs are computed numerically for single (left or right) and double censored data, and the extent of loss due to these censoring schemes is discussed. The rank tests considered here include among them the Mann-Whiney-Wilcoxon (MWW) test, the Savage test, and the linear combination of these two tests. In the case of all the tests, except the MWW test, it is found that the loss of efficiency due to left censoring is considerably less than that due to right censoring. In the case of finite samples, Monte Carlo simulation results showing the empirical levels and empirical powers against some Lehmann alternatives are presented.     

Statistics for a New Test of Discordance in Circular Data

This article considers the derivation of approximate distributions for two types of statistics that can be used in developing new tests of discordance in circular samples from the von Mises distribution. An alternative test of discordance is proposed based on the circular distance between sample points. The advantage of the test is that it allows users to detect possible outliers in both univariate and bivariate circular data. For illustration, the test is applied to two real circular data sets.     

中国月度数据的季节调整：一个新方案

 本文针对中国特定的节假日效应和交易日效应对季节调整问题提出了新的方案,包括移动节假日效应（如春节、中秋节、清明节、端午节等）、黄金周效应、五天工作制效应等;论文利用新的调整方案对我国社会消费品零售总额的月度数据进行了季节调整,诊断结果表明,新方案能比较充分地提取各种季节特征;论文对我国季节调整问题提出了针对性建议。