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Obtenção dos níveis de significância para os testes de Kruskal-Wallis, Friedman e comparações múltiplas não-paramétricas.; Obtaining significance levels for Kruskal-Wallis, Friedman and nonparametric multiple comparisons tests.

Pontes, Antonio Carlos Fonseca
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 29/06/2000 Português
Relevância na Pesquisa
36.34%
Uma das principais dificuldades encontradas pelos pesquisadores na utilização da Estatística Experimental Não-Paramétrica é a obtenção de resultados confiáveis. Os testes mais utilizados para os delineamentos com um fator de classificação simples inteiramente casualizados e blocos casualizados são o de Kruskal-Wallis e o de Friedman, respectivamente. As tabelas disponíveis para estes testes são pouco abrangentes, fazendo com que o pesquisador seja obrigado a recorrer a aproximações. Estas aproximações diferem dependendo do autor a ser consultado, podendo levar a resultados contraditórios. Além disso, tais tabelas não consideram empates, mesmo no caso de pequenas amostras. No caso de comparações múltiplas isto é mais evidente ainda, em especial quando ocorrem empates ou ainda, nos delineamentos inteiramente casualizados onde se tem número diferente de repetições entre tratamentos. Nota-se ainda que os softwares mais utilizados em geral recorrem a aproximações para fornecer os níveis de significância, além de não apresentarem resultados para as comparações múltiplas. Assim, o objetivo deste trabalho é apresentar um programa, em linguagem C, que realiza os testes de Kruskal-Wallis, de Friedman e de comparações múltiplas entre todos os tratamentos (bilateral) e entre os tratamentos e o controle (uni e bilateral) considerando todas as configurações sistemáticas de postos ou com 1.000.000 de configurações aleatórias...

Análise de variância multivariada com a utilização de testes não -paramétricos e componentes principais baseados em matrizes de postos.; Multivariate analysis of variance using nonparametric tests and principal components based on rank matrices.

Pontes, Antonio Carlos Fonseca
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 19/07/2005 Português
Relevância na Pesquisa
46.35%
Métodos não-paramétricos têm aplicação ampla na análise de dados, tendo em vista que não são limitados pela necessidade de imposição de distribuições populacionais específicas. O caráter multivariado de dados provenientes de estudos nas ciências do comportamento, ecológicos, experimentos agrícolas e muitos outros tipos, e o crescimento contínuo da tecnologia computacional, têm levado a um crescente interesse no uso de métodos multivariados não-paramétricos. A aplicação da análise de variância multivariada não-paramétrica é pouco inacessível ao pesquisador, exceto através de métodos aproximados baseados nos valores assintóticos da estatística de teste. Portanto, este trabalho tem por objetivo apresentar uma rotina na linguagem C que realiza testes baseados numa extensão multivariada do teste univariado de Kruskal- Wallis, usando a técnica das permutações. Para pequenas amostras, todas as configurações de tratamentos são obtidas para o cálculo do valor-p. Para grandes amostras, um número fixo de configurações aleatórias é usado, obtendo assim valores de significância aproximados. Além disso, um teste alternativo é apresentado com o uso de componentes principais baseados nas matrizes de postos.; Nonparametric methods have especially broad applications in the analysis of data since they are not bound by restrictions on the population distribution. The multivariate character of behavioural...

Nonparametric Tests for Treatment Effect Heterogeneity

Mitnik, Oscar K.; Imbens, Guido; Hotz, V. Joseph; Crump, Richard K.
Fonte: Elsevier Publicador: Elsevier
Português
Relevância na Pesquisa
46.24%
In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, that is, that there is no heterogeneity in average treatment effects by covariates. We derive tests that are straightforward to implement and illustrate the use of these tests on data from two sets of experimental evaluations of the effects of welfare-to-work programs.; Economics

Exact Nonparametric Two-Sample Homogeneity Tests for Possibly Discrete Distributions.

DUFOUR, Jean-Marie; FARHAT, Abdeljelil
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Artigo de Revista Científica Formato: 236458 bytes; application/pdf
Português
Relevância na Pesquisa
36.52%
In this paper, we study several tests for the equality of two unknown distributions. Two are based on empirical distribution functions, three others on nonparametric probability density estimates, and the last ones on differences between sample moments. We suggest controlling the size of such tests (under nonparametric assumptions) by using permutational versions of the tests jointly with the method of Monte Carlo tests properly adjusted to deal with discrete distributions. We also propose a combined test procedure, whose level is again perfectly controlled through the Monte Carlo test technique and has better power properties than the individual tests that are combined. Finally, in a simulation experiment, we show that the technique suggested provides perfect control of test size and that the new tests proposed can yield sizeable power improvements.; Dans ce texte, nous étudions plusieurs tests pour l’égalité de deux distributions inconnues. Deux de ces tests sont basés sur des fonctions de distribution empiriques, trois autres sur des estimateurs non paramétriques de fonctions de densité et les trois derniers sur des moments empiriques. Nous proposons de contrôler la taille des tests (sous des hypothèses non paramétriques) en employant des versions permutationnelles de ces tests conjointement avec la méthode des tests de Monte Carlo ajustée pour tenir compte de la possibilité de distributions discontinues. Nous proposons aussi une méthode pour combiner plusieurs de ces tests...

Designing Non-Parametric Estimates and Tests for Means

SCHLAG, Karl H.
Fonte: European University Institute Publicador: European University Institute
Tipo: Trabalho em Andamento Formato: 297272 bytes; application/pdf; digital
Português
Relevância na Pesquisa
36.4%
We show how to derive nonparametric estimates from results for Bernoulli distributions, provided the means are the only parameters of interest. The only information is that the support of each random variable is contained in a known bounded set. Examples include presenting minimax risk properties of the sample mean and a minimax regret estimate for costly treatment. With the same method we are able to design nonparametric exact statistical inference tests for means using existing uniformly most powerful (unbiased) tests for Bernoulli distributions. These tests are parameter most powerful in the sense that there is no alternative test with the same size that yields higher power over any set of alternatives that only depends on the means. As examples we present for the first time an exact unbiased nonparametric test for a single mean and for the equality of two means (both for independent samples and for paired experiments). We also show how to improve performance of Hannan consistent rules.

How to Attain Minimax Risk with Applications to Distribution-Free Nonparametric Estimation and Testing

SCHLAG, Karl H.
Fonte: European University Institute Publicador: European University Institute
Tipo: Trabalho em Andamento Formato: 405978 bytes; application/pdf; digital
Português
Relevância na Pesquisa
36.37%
We show how to a derive exact distribution-free nonparametric results for minimax risk when underlying random variables have known finite bounds and means are the only parameters of interest. Transform the data with a randomized mean preserving transformation into binary data and then apply the solution to minimax risk for the case where random variables are binary valued. This shows that minimax risk is attained by a linear strategy and the the set of binary valued distributions contains a least favorable prior. We apply these results to statistics. All unbiased symmetric non-randomized estimates for a function of the mean of a single sample are presented. We find a most powerful unbiased test for the mean of a single sample. We present tight lower bounds on size, type II error and minimal accuracy in terms of expected length of confidence intervals for a single mean and for the difference between two means. We show how to transform the randomized tests that attain the lower bounds into non-randomized tests that have at most twice the type I and II errors. Relative parameter efficiency can be measured in finite samples, in an example on anti-selfdealing indices relative (parameter) efficiency is 60% as compared to the tight lower bound. Our method can be used to generate distribution-free nonparametric estimates and tests when variance is the only parameter of interest. In particular we present a uniformly consistent estimator of standard deviation together with an upper bound on expected quadratic loss. We use our estimate to measure income inequality.

Estimating alternative technology sets in nonparametric efficiency analysis : restriction tests for panel and clustered data

NEUMANN, Anne; NIESWAND, Maria; SCHUBERT, Torben
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Trabalho em Andamento Formato: application/pdf; digital
Português
Relevância na Pesquisa
36.37%
Nonparametric efficiency analysis has become a widely applied technique to support industrial benchmarking as well as a variety of incentive-based regulation policies. In practice such exercises are often plagued by incomplete knowledge about the correct specifications of inputs and outputs. Simar and Wilson (2001) and Schubert and Simar (2011) propose restriction tests to support such specification decisions for cross-section data. However, the typical oligopolized market structure pertinent to regulation contexts often leads to low numbers of cross-section observations, rendering reliable estimation based on these tests practically unfeasible. This small-sample problem could often be avoided with the use of panel data, which would in any case require an extension of the cross-section restriction tests to handle panel data. In this paper we derive these tests. We prove the consistency of the proposed method and apply it to a sample of US natural gas transmission companies in 2003 through 2007. We find that the total quantity of gas delivered and gas delivered in peak periods measure essentially the same output. Therefore only one needs to be included. We also show that the length of mains as a measure of transportation service is non-redundant and therefore must be included.

Significant Testing in Nonparametric Regression based on the Bootstrap

Delgado, Miguel A.; González-Manteiga, Wenceslao
Fonte: Institute of Mathematical Statistics Publicador: Institute of Mathematical Statistics
Tipo: info:eu-repo/semantics/publishedVersion; info:eu-repo/semantics/article Formato: text/plain; application/pdf
Publicado em //2001 Português
Relevância na Pesquisa
36.38%
This paper proposes a test for selecting explanatory variables in nonparametric regression. The test does not need to estimate the conditional expectation function given al the variables, butonly those which are significant under the null hypothesis. This feature is computationally convenient and solves,in part the problem of the "curse of dimensionality" When seleccting regressors in a nonparametric context. The proposed test statistic is based on functional of a U-process. Contiguous alternatives, converging to the null at a rate n-1/2 can be detected. The asympotetic null distribution of the statistic depends on certain features of the data generating process, and asymptotic tests are difficult to implement except in rare circunstantces. We justify the consistency of two easy to implement bootstrap tests which exhibit good level accuracy for fairly small samples, according to the reported Monte Carlo simulation. These result are also applicable to test other interesting restictions on nonparametric curves, like partial linearity and conditional independence.

Nonparametric Tests for Conditional Symmetry in Dynamic Models

Delgado, Miguel A.; Escanciano, Juan Carlos
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica Formato: text/plain; application/pdf
Publicado em //2007 Português
Relevância na Pesquisa
46.07%
This article proposes omnibus tests for conditional symmetry around a parametric function in a dynamic context. Conditional moments may not exist or may depend on the explanatory variables. Test statistics are suitable functionals of the empirical process of residuals and explanatory variables, whose limiting distribution under the null is nonpivotal. The tests are implemented with the assistance of a bootstrap method, which is justified assuming very mild regularity conditions on the specification of the center of symmetry and the underlying serial dependence structure. Finite sample properties are examined by means of a Monte Carlo experiment.

A nonparametric copula based test for conditional independence with applications to granger causality

Bouezmarni, Taoufik; Rombouts, Jeroen V. K.; Taamouti, Abderrahim
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /06/2009 Português
Relevância na Pesquisa
46.19%
This paper proposes a new nonparametric test for conditional independence, which is based on the comparison of Bernstein copula densities using the Hellinger distance. The test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the data. In fact, to apply the test, only a bandwidth is needed for the nonparametric copula. We prove that the test statistic is asymptotically pivotal under the null hypothesis, establish local power properties, and motivate the validity of the bootstrap technique that we use in finite sample settings. A simulation study illustrates the good size and power properties of the test. We illustrate the empirical relevance of our test by focusing on Granger causality using financial time series data to test for nonlinear leverage versus volatility feedback effects and to test for causality between stock returns and trading volume. In a third application, we investigate Granger causality between macroeconomic variables

Sign Tests for Long-memory Time Series

Delgado, Miguel A.; Velasco, Carlos
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica Formato: text/plain; application/pdf
Publicado em //2005 Português
Relevância na Pesquisa
46.11%
This paper proposes sign-based tests for simple and composite hypotheses on the long-memory parameter of a time series process. The tests allow for nonstationary hypothesis, such as unit root, as well as for stationary hypotheses, such as weak dependence or no integration. The proposed generalized Lagrange multiplier sign tests for simple hypotheses on the long-memory parameter are exact and locally optimal among those in their class. We also propose tests for composite hypotheses on the parameters of ARFIMA processes. The resulting tests statistics have a standard normal limiting distribution under the null hypothesis.

Significance testing in nonparametric regression base on the bootstrap

Delgado, Miguel A.; González-Manteiga, Wenceslao
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /10/1998 Português
Relevância na Pesquisa
36.4%
We propose a test for selecting explanatory variables in nonparametric regression. The test does not need to estimate the conditional expectation function given all the variables but only those which are significant under the null hypothesis. This feature is compntationally convenient and solves, in part, the problem of the "curse of dimensionality" when selecting regressors in a nonparametric context. The proposed test statistic is based on functionals of an empirical process marked by nonparametric residuals. Contiguous alternatives, converging to the null at a rate n-1I2 can be detected. The asymptotic null distribution of the statistic depends on certain features of the data generating process, and asymptotic tests are difficult to implement except in rare circumstances. We justify the consistency of two bootstrap tests easy to implement, which exhibit good level accuracy for fairly small samples, according to the Monte Carlo simulations reported. These results are also applicable to test other interesting restrictions on nonparametric regression curves, like partial linearity and conditional independence.

Firms´productivity and the export market: a nonparametric approach

Delgado, Miguel A.; Fariñas, José C.; Ruano, Sonia
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /06/1999 Português
Relevância na Pesquisa
56.24%
This paper examines total factor productivity differences between exporting and nonexporting firms. These differences are documented on the basis of a sample of Spanish manufacturing firms over the period 1991-1996 drawn from the ESEE. The paper also examines two complementary explanations for the superior productivity of exporting firms: 1) the market selection hypothesis, and 2) the learning hypothesis. Nonparametric tests are implemented for testing these hypothesis. Results indicate clearly higher levels of productivity for exporting firms than for non-exporting firms. Evidence favours the hypothesis of self-selection of more efficient firms into the export market. However there is little evidence that these efficiency gains are supportive of the learning-by-exporting hypothesis.

Nonparametric tests for conditional independence using conditional distributions

Bouezmarni, Taoufik; Taamouti, Abderrahim
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: info:eu-repo/semantics/draft; info:eu-repo/semantics/workingPaper Formato: text/plain; application/pdf
Publicado em 06/01/2012 Português
Relevância na Pesquisa
56.24%
The concept of causality is naturally defined in terms of conditional distribution, however almost all the empirical works focus on causality in mean. This paper aim to propose a nonparametric statistic to test the conditional independence and Granger non-causality between two variables conditionally on another one. The test statistic is based on the comparison of conditional distribution functions using an L2 metric. We use Nadaraya-Watson method to estimate the conditional distribution functions. We establish the asymptotic size and power properties of the test statistic and we motivate the validity of the local bootstrap. Further, we ran a simulation experiment to investigate the finite sample properties of the test and we illustrate its practical relevance by examining the Granger non-causality between S&P 500 Index returns and VIX volatility index. Contrary to the conventional t-test, which is based on a linear mean-regression model, we find that VIX index predicts excess returns both at short and long horizons.; Financial support from the Natural Sciences and Engineering Research Council of Canada and from the Spanish Ministry of Education through grants SEJ 2007-63098 are also acknowledged

Conditional stochastic dominance tests in dynamic settings

Gonzalo, Jesús; Olmo, José
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: info:eu-repo/semantics/draft; info:eu-repo/semantics/workingPaper Formato: application/pdf; application/pdf; application/pdf; text/plain
Publicado em /07/2013 Português
Relevância na Pesquisa
36.34%
This paper proposes nonparametric consistent tests of conditional stochastic dominance of arbitrary order in a dynamic setting. The novelty of these tests lies in the nonparametric manner of incorporating the information set. The test allows for general forms of unknown serial and mutual dependence between random variables, and has an asymptotic distribution that can be easily approximated by simulation. This method has good finite-sample performance. These tests are applied to determine investment efficiency between US industry portfolios conditional on the dynamics of the market portfolio. The empirical analysis suggests that Telecommunications dominates the other sectoral portfolios under risk aversion.

An overview of nonparametric tests of extreme-value dependence and of some related statistical procedures

Bücher, Axel; Kojadinovic, Ivan
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 24/10/2014 Português
Relevância na Pesquisa
46.2%
An overview of existing nonparametric tests of extreme-value dependence is presented. Given an i.i.d.\ sample of random vectors from a continuous distribution, such tests aim at assessing whether the underlying unknown copula is of the {\em extreme-value} type or not. The existing approaches available in the literature are summarized according to how departure from extreme-value dependence is assessed. Related statistical procedures useful when modeling data with this type of dependence are briefly described next. Two illustrations on real data sets are then carried out using some of the statistical procedures under consideration implemented in the \textsf{R} package {\tt copula}. Finally, the related problem of testing the {\em maximum domain of attraction} condition is discussed.; Comment: 21 pages, 1 table

Nonparametric tests for pathwise properties of semimartingales

Cont, Rama; Mancini, Cecilia
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/04/2011 Português
Relevância na Pesquisa
46.37%
We propose two nonparametric tests for investigating the pathwise properties of a signal modeled as the sum of a L\'{e}vy process and a Brownian semimartingale. Using a nonparametric threshold estimator for the continuous component of the quadratic variation, we design a test for the presence of a continuous martingale component in the process and a test for establishing whether the jumps have finite or infinite variation, based on observations on a discrete-time grid. We evaluate the performance of our tests using simulations of various stochastic models and use the tests to investigate the fine structure of the DM/USD exchange rate fluctuations and SPX futures prices. In both cases, our tests reveal the presence of a non-zero Brownian component and a finite variation jump component.; Comment: Published in at http://dx.doi.org/10.3150/10-BEJ293 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

spTest: An R Package Implementing Nonparametric Tests of Isotropy

Weller, Zachary D.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.34%
An important step of modeling spatially-referenced data is appropriately specifying the second order properties of the random field. A scientist developing a model for spatial data has a number of options regarding the nature of the dependence between observations. One of these options is deciding whether or not the dependence between observations depends on direction, or, in other words, whether or not the spatial covariance function is isotropic. Isotropy implies that spatial dependence is a function of only the distance and not the direction of the spatial separation between sampling locations. A researcher may use graphical techniques, such as directional sample semivariograms, to determine whether an assumption of isotropy holds. These graphical diagnostics can be difficult to assess, subject to personal interpretation, and potentially misleading as they typically do not include a measure of uncertainty. In order to escape these issues, a hypothesis test of the assumption of isotropy may be more desirable. To avoid specification of the covariance function, a number of nonparametric tests of isotropy have been developed using both the spatial and spectral representations of random fields. Several of these nonparametric tests are implemented in the R package spTest...

Nonparametric tests for change-point detection in the distribution of block maxima based on probability weighted moments

Kojadinovic, Ivan; Naveau, Philippe
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/07/2015 Português
Relevância na Pesquisa
46.2%
The analysis of seasonal or annual block maxima is of interest in fields such as hydrology, climatology or meteorology. In connection with the celebrated method of block maxima, we study several nonparametric tests that can be used to assess whether the available series of maxima is identically distributed. It is assumed that block maxima are independent but not necessarily generalized extreme value distributed. The asymptotic null distributions of the test statistics are investigated and the practical computation of approximate p-values is addressed. Extensive Monte-Carlo simulations show the adequate finite-sample behavior of the studied tests for a large number of realistic data generating scenarios. Illustrations on several environmental datasets conclude the work.; Comment: 35 pages, 8 tables

Nonparametric Tests for Treatment Effect Heterogeneity

Hotz, V.J.; Crump, Richard; Imbens, Guido; Mitnik, Oscar
Fonte: Review of Economics and Statistics Publicador: Review of Economics and Statistics
Tipo: Artigo de Revista Científica Formato: 233881 bytes; application/pdf
Publicado em //2008 Português
Relevância na Pesquisa
46.2%
A large part of the recent literature on program evaluation has focused on estimation of the average effect of the treatment under assumptions of unconfoundedness or ignorability following the seminal work by Rubin (1974) and Rosenbaum and Rubin (1983). In many cases however, researchers are interested in the effects of programs beyond estimates of the overall average or the average for the subpopulation of treated individuals. It may be of substantive interest to investigate whether there is any subpopulation for which a program or treatment has a nonzero average effect, or whether there is heterogeneity in the effect of the treatment. The hypothesis that the average effect of the treatment is zero for all subpopulations is also important for researchers interested in assessing assumptions concerning the selection mechanism. In this paper we develop two nonparametric tests. The first test is for the null hypothesis that the treatment has a zero average effect for any subpopulation defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, in other words, that there is no heterogeneity in average treatment effects by covariates. Sacrificing some generality by focusing on these two specific null hypotheses we derive tests that are straightforward to implement.