Browsing by Author "Loschi, Rosangela H."

Now showing 1 - 6 of 6
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    A note on extendibility and predictivistic inference in finite populations
    (2009) Iglesias, Pilar L.; Loschi, Rosangela H.; Pereira, Carlos A. B.; Wechsler, Sergio
    The usual finite population model where information provided by a subset of units is used to reduce uncertainty about functions of the complete population list of values is explored from a predictivistic point of view. Under this approach, only operationally meaningful quantities (operational parameters) are considered and therefore no superpopulation parameters are involved. This paper addresses the estimation of both population total and maximum based on uniformity and/or exchangeability judgments on finite sequences of random variables. A central point of this paper is that there are contexts in which the superpopulation approach cannot be employed in inferential problems in finite populations. There are circumstances in which the prior distributions for the operational parameters cannot be obtained from any superpopulation model. Conditions for the extendibility to infinite populations are also established for some models, as this approach may ease the inferential problem.
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    Change Point Detection in The Skew-Normal Model Parameters
    (2013) Arellano Valle, Reinaldo Boris; Castro Cepero, Luis Mauricio; Loschi, Rosangela H.
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    Maximum likelihood methods in a robust censored errors-in-variables model
    (2015) Rocha, Gustavo H. M. A.; Arellano Valle, Reinaldo Boris; Loschi, Rosangela H.
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    Multipartition model for multiple change point identification
    (2023) Pedroso, Ricardo C.; Loschi, Rosangela H.; Quintana, Fernando Andres
    The product partition model (PPM) is widely used for detecting multiple change points. Because changes in different parameters may occur at different times, the PPM fails to identify which parameters experienced the changes. To solve this limitation, we introduce a multipartition model to detect multiple change points occurring in several parameters. It assumes that changes experienced by each parameter generate a different random partition along the time axis, which facilitates identifying those parameters that changed and the time when they do so. We apply our model to detect multiple change points in Normal means and variances. Simulations and data illustrations show that the proposed model is competitive and enriches the analysis of change point problems.
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    Shape mixtures of multivariate skew-normal distributions
    (ELSEVIER INC, 2009) Arellano Valle, Reinaldo B.; Genton, Marc G.; Loschi, Rosangela H.
    Classes of shape mixtures of independent and dependent multivariate skew-normal distributions are considered and some of their main properties are studied. If interpreted from a Bayesian point of view, the results obtained in this paper bring tractability to the problem of inference for the shape parameter, that is, the posterior distribution can be written in analytic form. Robust inference for location and scale parameters is also obtained under particular conditions. (C) 2008 Elsevier Inc. All rights reserved.
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    Test procedures based on combination of Bayesian evidences for H-0
    (BRAZILIAN STATISTICAL ASSOCIATION, 2012) Loschi, Rosangela H.; Santos, Cristiano C.; Arellano Valle, Reinaldo B.
    We introduce two procedures for testing which are based on pooling the posterior evidence for the null hypothesis provided by the full Bayesian significance test and the posterior probability for the null hypothesis. Although the proposed procedures can be used in more general situations, we focus attention in tests for a precise null hypothesis. We prove that the proposed procedure based on the linear operator is a Bayes rule. We also verify that it does not lead to the Jeffreys Lindley paradox. For a precise null hypothesis, we prove that the procedure based on the logarithmic operator is a generalization of Jeffreys test. We apply the results to some well-known probability families. The empirical results show that the proposed procedures present good performances. As a by-product we obtain tests for normality under the skew-normal one.