arXiv subjects

Leandro Pardo

Publications and source records attributed to Leandro Pardo.

49 records · Page 3Linked to original sources

arXiv2015

Empirical phi-divergence test statistics for testing simple null hypotheses based on exponentially tilted empirical likelihood estimators

In Econometrics, imposing restrictions without assuming underlying distributions to modelize complex realities is a valuable methodological tool. However, if a subset of restrictions were not correctly specified, the usual test-statistics for correctly specified models tend to reject erronously a simple null hypothesis. In this setting, we may say that the model suffers from misspecification. We study the behavior of empirical phi-divergence test-statistics, introduced in Balakrishnan et al. (2015), by using the exponential tilted empirical likelihood estimators of Schennach (2007), as a good compromise between efficiency of the significance level for small sample sizes and robustness under misspecification.

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arXiv2015

New improved estimators for overdispersion in models with clustered multinomial data and unequal cluster sizes

It is usual to rely on the quasi-likelihood methods for deriving statistical methods applied to clustered multinomial data with no underlying distribution. Even though extensive literature can be encountered for these kind of data sets, there are few investigations to deal with unequal cluster sizes. This paper aims to contribute to fill this gap by proposing new estimators for the intracluster correlation coefficient.

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arXiv2015

Generalized Wald-type Tests based on Minimum Density Power Divergence Estimators

In testing of hypothesis the robustness of the tests is an important concern. Generally, the maximum likelihood based tests are most efficient under standard regularity conditions, but they are highly non-robust even under small deviations from the assumed conditions. In this paper we have proposed generalized Wald-type tests based on minimum density power divergence estimators for parametric hypotheses. This method avoids the use of nonparametric density estimation and the bandwidth selection. The trade-off between efficiency and robustness is controlled by a tuning parameter $β$. The asymptotic distributions of the test statistics are chi-square with appropriate degrees of freedom. The performance of the proposed tests are explored through simulations and real data analysis.

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arXiv2014

On the Robustness of a Divergence based Test of Simple Statistical Hypotheses

The most popular hypothesis testing procedure, the likelihood ratio test, is known to be highly non-robust in many real situations. Basu et al. (2013a) provided an alternative robust procedure of hypothesis testing based on the density power divergence; however, although the robustness properties of the latter test were intuitively argued for by the authors together with extensive empirical substantiation of the same, no theoretical robustness properties were presented in this work. In the present paper we will consider a more general class of tests which form a superfamily of the procedures described by Basu et al. (2013a). This superfamily derives from the class of $S$-divergences recently proposed by Basu et al. (2013a). In this context we theoretically prove several robustness results of the new class of tests and illustrate them in the normal model. All the theoretical robustness properties of the Basu et al. (2013a) proposal follows as special cases of our results.

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arXiv2014

Robust Tests for the Equality of Two Normal Means based on the Density Power Divergence

Statistical techniques are used in all branches of science to determine the feasibility of quantitative hypotheses. One of the most basic applications of statistical techniques in comparative analysis is the test of equality of two population means, generally performed under the assumption of normality. In medical studies, for example, we often need to compare the effects of two different drugs, treatments or preconditions on the resulting outcome. The most commonly used test in this connection is the two sample $t$-test for the equality of means, performed under the assumption of equality of variances. It is a very useful tool, which is widely used by practitioners of all disciplines and has many optimality properties under the model. However, the test has one major drawback; it is highly sensitive to deviations from the ideal conditions, and may perform miserably under model misspecification and the presence of outliers. In this paper we present a robust test for the two sample hypothesis based on the density power divergence measure (Basu et al., 1998), and show that it can be a great alternative to the ordinary two sample $t$-test. The asymptotic properties of the proposed tests are rigorously established in the paper, and their performances are explored through simulations and real data analysis.

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arXiv2014

Comparing two treatments in terms of the likelihood ratio order

In this paper new families of test statistics are introduced and studied for the problem of comparing two treatments in terms of the likelihood ratio order. The considered families are based on phi-divergence measures and arise as natural extensions of the classical likelihood ratio test and Pearson test statistics. It is proven that their asymptotic distribution is a common chi-bar random variable. An illustrative example is presented and the performance of these statistics is analysed through a simulation study. Through a simulation study it is shown that, for most of the proposed scenarios adjusted to be small or moderate, some members of this new family of test-statistic display clearly better performance with respect to the power in comparison to the classical likelihood ratio and the Pearson's chi-square test while the exact size remains closed to the nominal size. In view of the exact powers and significance levels, the study also shows that the Wilcoxon test-statistic is not as good as the two classical test-statistics.

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arXiv2014

Empirical phi-divergence test statistics for testing simple and composite null hypotheses

The main purpose of this paper is to introduce first a new family of empirical test statistics for testing a simple null hypothesis when the vector of parameters of interest are defined through a specific set of unbiased estimating functions. This family of test statistics is based on a distance between two probability vectors, with the first probability vector obtained by maximizing the empirical likelihood on the vector of parameters, and the second vector defined from the fixed vector of parameters under the simple null hypothesis. The distance considered for this purpose is the phi-divergence measure. The asymptotic distribution is then derived for this family of test statistics. The proposed methodology is illustrated through the well-known data of Newcomb's measurements on the passage time for light. A simulation study is carried out to compare its performance with that of the empirical likelihood ratio test when confidence intervals are constructed based on the respective statistics for small sample sizes. The results suggest that the "empirical modified likelihood ratio test statistic" provides a competitive alternative to the empirical likelihood ratio test statistic, and is also more robust than the empirical likelihood ratio test statistic in the presence of contamination in the data. Finally, we propose empirical phi-divergence test statistics for testing a composite null hypothesis and present some asymptotic as well as simulation results for evaluating the performance of these test procedures.

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arXiv2014

Phi-Divergence test statistics for testing the validity of latent class models for binary data

The main purpose of this paper is to present new families of test statistics for studying the problem of goodness-of-fit of some data to a latent class model for binary data. The families of test statistics introduced are based on phi-divergence measures, a natural extension of maximum likelihood. We also treat the problem of testing a nested sequence of latent class models for binary data. For these statistics, we obtain their asymptotic distribution. We shall consider consistent estimators introduced in Felipe et al (2014) for solving the problem of estimation. Finally, a simulation study is carried out in order to compare the efficiency, in the sense of the level and the power, of the new statistics considered in this paper for sample sizes that are not big enough to apply the asymptotical results.

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arXiv2014

Minimum ϕ-divergence estimation in constrained latent class models for binary data

The main purpose of this paper is to introduce and study the behavior of minimum ϕ-divergence estimators as an alternative to the maximum likelihood estimator in latent class models for binary items. As it will become clear below, minimum ϕ-divergence estimators are a natural extension of the maximum likelihood estimator. The asymptotic properties of minimum ϕ-divergence estimators for latent class models for binary data are developed. Finally, to compare the efficiency and robustness of these new estimators with those obtained through maximum likelihood when the sample size is not big enough to apply the asymptotic results, we have carried out a simulation study.

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arXiv2014

Phi-divergence statistics for the likelihood ratio order: an approach based on log-linear models

When some treatments are ordered according to the categories of an ordinal categorical variable (e.g., extent of side effects) in a monotone order, one might be interested in knowing wether the treatments are equally effective or not. One way to do that is to test if the likelihood ratio order is strictly verified. A method based on log-linear models is derived to make statistical inference and phi-divergence test-statistics are proposed for the test of interest. Focussed on loglinear modeling, the theory associated with the asymptotic distribution of the phi-divergence test-statistics is developed. An illustrative example motivates the procedure and a simulation study for small and moderate sample sizes shows that it is possible to find phi-divergence test-statistic with an exact size closer to nominal size and higher power in comparison with the classical likelihood ratio.

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arXiv2011

Change point analysis of an exponential model based on Phi-divergence test-statistics: simulated critical points case

Recently Batsidis \textit{et al.} (2011) have presented a new procedure based on divergence measures for testing the hypothesis of the existence of a change point in exponential populations. A simulation study was carried out, in this paper, using the asymptotic critical points obtained from the asymptotic distribution of the new test statistics introduced there. The main purpose of this paper is to study the behavior of the test statistics introduced in the cited paper of Batsidis \textit{et al.} (2011), using simulated critical points.

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arXiv2011

A procedure for the change point problem in parametric models based on phi-divergence test-statistics

This paper studies the change point problem for a general parametric, univariate or multivariate family of distributions. An information theoretic procedure is developed which is based on general divergence measures for testing the hypothesis of the existence of a change. For comparing the accuracy of the new test-statistic a simulation study is performed for the special case of a univariate discrete model. Finally, the procedure proposed in this paper is illustrated through a classical change-point example.

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arXiv2010

Poisson loglinear modeling with linear constraints on the expected cell frequencies

In this paper we consider Poisson loglinear models with linear constraints (LMLC) on the expected table counts. Multinomial and product multinomial loglinear models can be obtained by considering that some marginal totals (linear constraints on the expected table counts) have been prefixed in a Poisson loglinear model. Therefore with the theory developed in this paper, multinomial and product multinomial loglinear models can be considered as a particular case. To carry out inferences on the parameters in the LMLC an information-theoretic approach is followed from which the classical maximum likelihood estimators and Pearson chi-square statistics for goodness-of fit are obtained. In addition, nested hypotheses are proposed as a general procedure for hypothesis testing. Through a simulation study the appropriateness of proposed inference tools is illustrated.

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