非参数检验在市场调查中的应用

本文分析了非参数检验在市场调查中应用,并结合一些市场调查的数据资料对几种常用的非参数检验方法做了实证分析。     

非参数χ~2检验的两个局限性问题

χ2检验在参数检验和非参数检验有着广泛的应用,但在教学和科研中,我们发现χ2检验应用功能被扩大化了,教学和科研人员往往忽略了使用χ2检验的前提条件和应用的局限性。本文着重讨论χ2检验在非参数检验中的两个比较严重的局限性问题,并提出了相应的解决方法,以期提高统计方法应用的准确性。     

非参数X^2检验的两个局限性问题

X^2检验在参数检验和非参数检验有着广泛的应用，但在教学和科研中，我们发现X^2检验应用功能被扩大化了，教学和科研人员往往忽略了使用X^2检验的前提条件和应用的局限性。本文着重讨论X^2检验在非参数检验中的两个比较严重的局限性问题，并提出了相应的解决方法．以期提高统计方法应用的准确性．     

对养老院工作人员满意度调查分析

本文对杭州市养老院工作人员满意度进行调查，运用非参数检验的方法，分析引起工作人员满意度差异的主要因素，最后为改善养老院工作人员的工作环境和提高养老院服务质量提供对策建议。     

多个独立样本非参数检验中的Jonckheere-Terpstra方法

Jonckheere-Terpstra检验在多个独立样本非参数检验中,可以检验多个独立样本的位置参数是否持续上升和下降,和常用的Kruskal-Wallis检验相比,Jonckheere-Terpstra检验的计算过程相对复杂,文章对Jonckheere-Terpstra检验统计量进行了梳理,然后结合案例介绍了Jonckheere-Terpstra检验的实际应用.     

甘肃省消费者维权意识的调查与分析

本文在对甘肃省消费者维权意识进行调查的基础上，利用SPSS11.5对调查结果进行汇总分析，以了解甘肃省消费者权益保护现状以及消费者维护自身权益的意识，并结合必要的非参数检验统计量对具体结论予以验证，以期结论更加准确可靠，为相关部门提供政策依据，     

双样本尺度参数的四种非参数检验方法

在检验两个样本的尺度参数也就是它们的离散程度是否时，有4种常用的非参数检验方法，即Mood检验、Ansafi—Bradley检验、Siegel—Turkey检验和Klotz检验，但是这4种检验方法的原理分散在一些非参数检验的教材中。文章对这4种检验方法的检验原理进行了整理，并且结合实例进行了分析。     

非线性协和模型:非参数方法研究综述

对于非线性协和理论,非参数方法具有重要的研究和应用意义。文章对当前非线性协和理论的三个研究方向进行了梳理,进一步对已给出的非线性协和的不同定义,总结了相应的具有非线性结构的时间序列的非平稳性非参数检验、非线性协和关系的非参数检验、非线性协和模型非参数估计方法以及非线性误差修正模型的非参数方法前沿问题。     

中小板IPO抑价和所有权保留关系的实证研究

文章借鉴Robinson等首次提出的所有权保留和抑价间的曲线关系理论模型对全流通后的中国中小板IPO市场数据进行实证。通过非参数检验和回归分析,得到了和该理论模型近似一致的结论:中国中小板上市公司的控股股东在IPO发行前所决定的所有权保留比例和IPO抑价有近似的曲线关系。     

多选题的统计分析及其SPSS实现

目前对于多选项问题资料的数据分析多限于频数描述统计,有些统计软件如SPSS也有关于多重应答统计模块,但也仅限于多重应答分类法数据资料和二分类数据资料的频数描述.文章亦采用非参数检验中的Cochran检验来处理多选题中二分类数据资料,对问题进行推断.     

Simultaneous tests for homogeneity of two zero-inflated (beta) populations

ABSTRACT

Motivated by an example in marine science, we use Fisher’s method to combine independent likelihood ratio tests (LRTs) and asymptotic independent score tests to assess the equivalence of two zero-inflated Beta populations (mixture distributions with three parameters). For each test, test statistics for the three individual parameters are combined into a single statistic to address the overall difference between the two populations. We also develop non parametric and semiparametric permutation-based tests for simultaneously comparing two or three features of unknown populations. Simulations show that the likelihood-based tests perform well for large sample sizes and that the statistics based on combining LRT statistics outperforms the ones based on combining score test statistics. The permutation-based tests have overall better performance in terms of both power and type I error rate. Our methods are easy to implement and computationally efficient, and can be expanded to more than two populations and to other multiple parameter families. The permutation tests are entirely generic and can be useful in various applications dealing with zero (or other) inflation.     

Permutation tests for homogeneity based on some characterizations

In this work, non parametric tests are proposed for testing the homogeneity of two or more populations. The tests are based on recently obtained characterizations. The test procedure is based on the permutation bootstrap technique. For the two-sample case the new tests are compared with permutation tests based on the empirical characteristic function and some other tests. The comparison is fulfilled via a Monte Carlo simulation.     

Non Null Asymptotic Distributions of Some Criteria in Censored Exponential Regression

In this article, we consider the class of censored exponential regression models which is very useful for modeling lifetime data. Under a sequence of Pitman alternatives, the asymptotic expansions up to order n^{? 1/2} of the non null distribution functions of the likelihood ratio, Wald, Rao score, and gradient statistics are derive in this class of models. The non null asymptotic distribution functions of these statistics are obtained for testing a composite null hypothesis in the presence of nuisance parameters. The power of all four tests, which are equivalent to first order, are compared based on these non null asymptotic expansions. Furthermore, in order to compare the finite-sample performance of these tests in this class of models, we consider Monte Carlo simulations. We also present an empirical application for illustrative purposes.     

Testing Equality of Order Constrained Inverse Gaussian Means

The inverse Gaussian family of non negative, skewed random variables is analytically simple, and its inference theory is well known to be analogous to the normal theory in numerous ways. Hence, it is widely used for modeling non negative positively skewed data. In this note, we consider the problem of testing homogeneity of order restricted means of several inverse Gaussian populations with a common unknown scale parameter using an approach based on the classical methods, such as Fisher's, for combining independent tests. Unlike the likelihood approach which can only be readily applied to a limited number of restrictions and the settings of equal sample sizes, this approach is applicable to problems involving a broad variety of order restrictions and arbitrary sample size settings, and most importantly, no new null distributions are needed. An empirical power study shows that, in case of the simple order, the test based on Fisher's combination method compares reasonably with the corresponding likelihood ratio procedure.     

The Use of Permutation Tests for Variance Components in Linear Mixed Models

Standard asymptotic chi-square distribution of the likelihood ratio and score statistics under the null hypothesis does not hold when the parameter value is on the boundary of the parameter space. In mixed models it is of interest to test for a zero random effect variance component. Some available tests for the variance component are reviewed and a new test within the permutation framework is presented. The power and significance level of the different tests are investigated by means of a Monte Carlo simulation study. The proposed test has a significance level closer to the nominal one and it is more powerful.     

Testing parameter of the power series distribution of a zero inflated power series model

The Zero Inflated Power Series Distribution (ZIPSD) contains two parameters. The first parameter indicates inflation of zero and the other parameter is that of the Power Series distribution. We provide three asymptotic tests for testing the parameter of Power Series distribution, using an unconditional (standard) likelihood approach, a conditional likelihood approach and a test based on sample mean, respectively. The performance of these three tests has been studied for Zero Inflated Poisson Distribution (ZIPD). Asymptotic Confidence Intervals for the parameter are also provided.     

Goodness-of-Fit Tests for the Gamma Distribution Based on the Empirical Laplace Transform

We propose a class of goodness-of-fit tests for the gamma distribution that utilizes the empirical Laplace transform. The consistency of the tests as well as their asymptotic distribution under the null hypothesis are investigated. As the decay of the weight function tends to infinity, the test statistics approach limit values related to the first non zero component of Neyman's smooth test for the gamma law. The new tests are compared with other omnibus tests for the gamma distribution.     

Default Bayesian goodness-of-fit tests for the skew-normal model

In this paper we propose a series of goodness-of-fit tests for the family of skew-normal models when all parameters are unknown. As the null distributions of the considered test statistics depend only on asymmetry parameter, we used a default and proper prior on skewness parameter leading to the prior predictive p-value advocated by G. Box. Goodness-of-fit tests, here proposed, depend only on sample size and exhibit full agreement between nominal and actual size. They also have good power against local alternative models which also account for asymmetry in the data.