Person: Morales González, Domingo
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First Name
Domingo
Last Name
Morales González
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Matemáticas
Department
Area
Estadística e Investigación Operativa
Identifiers
19 results
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Now showing 1 - 10 of 19
Item Asymptotic distributions of phi-divergences of hypothetical and observed frequencies on refined partitions(Statistica Neerlandica, 1988) Menéndez Calleja, María Luisa; Morales González, Domingo; Pardo Llorente, Leandro; Vadja, IgorFor a wide class of goodness-of-fit statistics based on phi-divergences between hypothetical cell probabilities and observed relative frequencies, the asymptotic normality is established under the assumption n/m(n) --> gamma is an element of (0, infinity), where n denotes sample size and m(n) the number of cells. Related problems of asymptotic distributions of phi-divergence errors, and of phi-divergence deviations of histogram estimators from their expected values, are considered too.Item Divergence-based confidence intervals in false-positive misclassification model(Journal of Statistical Computation and Simulation, 2008) Martín Apaolaza, Níriam; Morales González, Domingo; Pardo Llorente, LeandroIn this article, we introduce minimum divergence estimators of parameters of a binary response model when data are subject to false-positive misclassification and obtained using a double-sampling plan. Under this set up, the problem of goodness-of-fit is considered and divergence-based confidence intervals (CIs) for a population proportion parameter are derived. A simulation experiment is carried out to compare the coverage probabilities of the new CIs. An application to real data is also given.Item Extension of the Wald statistic to models with dependent observations(Metrika, 2000) Morales González, Domingo; Pardo Llorente, Leandro; Pardo Llorente, María del Carmen; Vadja, IgorA generalization of the Wald statistic for testing composite hypotheses is suggested for dependent data from exponential models which include Levy processes and diffusion fields. The generalized statistic is proved to be asymptotically chi-squared distributed under regular composite hypotheses. It is simpler and more easily available than the generalized likelihood ratio statistic. Simulations in an example where the latter statistic is available show that the generalized Wald test achieves higher average power than the generalized likelihood ratio test.Item Informational distances and related statistics in mixed continuous and categorical variables(Journal of Statistical Planning and Inference, 1998) Morales González, Domingo; Pardo Llorente, Leandro; Zografos, KonstantinosA general class of dissimilarity measures among k greater than or equal to 2 distributions and their sample estimators are considered, for mixed continuous and categorical variables. The distributional properties are studied for the location model and the asymptotic distributions are investigated, in the general parametric case. The asymptotic distributions of the resulting statistics are used in various settings, to test statistical hypotheses.Item Divergence-based estimation and testing with misclassified data(Statistical Papers, 2005) Landaburu Jiménez, María Elena; Morales González, Domingo; Pardo Llorente, LeandroThe well-known chi-squared goodness-of-fit test for a multinomial distribution is generally biased when the observations are subject to misclassification. In Pardo and Zografos (2000) the problem was considered using a double sampling scheme and phi-divergence test statistics. A new problem appears if the null hypothesis is not simple because it is necessary to give estimators for the unknown parameters. In this paper the minimum phi-divergence estimators are considered and some of their properties are established. The proposed phi-divergence test statistics are obtained by calculating phi-divergences between probability density functions and by replacing parameters by their minimum phi-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an exampleItem Rényi statistics for testing composite hypotheses in general exponential models.(Statistics, 2004) Morales González, Domingo; Pardo Llorente, Leandro; Pardo Llorente, María del Carmen; Vadja, IgorWe introduce a family of Renyi statistics of orders r is an element of R for testing composite hypotheses in general exponential models, as alternatives to the previously considered generalized likelihood ratio (GLR) statistic and generalized Wald statistic. If appropriately normalized exponential models converge in a specific sense when the sample size (observation window) tends to infinity, and if the hypothesis is regular, then these statistics are shown to be chi(2)-distributed under the hypothesis. The corresponding Renyi tests are shown to be consistent. The exact sizes and powers of asymptotically alpha-size Renyi, GLR and generalized Wald tests are evaluated for a concrete hypothesis about a bivariate Levy process and moderate observation windows. In this concrete situation the exact sizes of the Renyi test of the order r = 2 practically coincide with those of the GLR and generalized Wald tests but the exact powers of the Renyi test are on average somewhat better.Item Renyi statistics in directed families of exponential experiments(Statistics, 2000) Morales González, Domingo; Pardo Llorente, Leandro; Vadja, IgorRenyi statistics are considered in a directed family of general exponential models. These statistics are defined as Renyi distances between estimated and hypothetical model. An asymptotically quadratic approximation to the Renyi Statistics is established, leading to similar asymptotic distribution results as established in the literature For the likelihood ratio statistics. Some arguments in favour of the Renyi statistics are discussed, and a numerical comparison of the Renyi goodness-of-fit tests with the Likelihood ratio test is presented.Item Some approximations to power functions of phi-divergence tests in parametric models(Test, 2001) Morales González, Domingo; Pardo Llorente, LeandroIn Morales et al (1997) phi-divergence statistics were proposed for testing hypotheses in general populations. In this paper we present some approximations to the power function of these new tests statistics for the cases of the simple null hypotheses and the composite null hypotheses.Item Approximations to powers of phi-disparity goodness-of-fit tests(Communications in statistics. Theory and methods, 2001) Menéndez Calleja, María Luisa; Morales González, Domingo; Pardo Llorente, Leandro; Vadja, IgorThe paper studies a class of tests based on disparities between the real-valued data and theoretical models resulting either from fixed partitions of the observation space, or from the partitions by the sample quantiles of fixed orders. In both cases there are considered the goodness-of-fit tests of simple and composite hypotheses. All tests are shown to be consistent, and their power is evaluated at the nonlocal as well as local alternatives.Item Limit laws for disparities of spacings(Journal of Nonparametric Statistics, 2003) Morales González, Domingo; Pardo Llorente, Leandro; Pardo Llorente, María del Carmen; Vadja, IgorDisparities of spacings mean the phi-disparities D-phi((q) over bar (n), p(n)) of discrete hypothetical and empirical distributions g and p(n) defined by m-spacings on i.i.d. samples of size n where phi: (0, infinity) \--> HR is twice continuously differentiable in a neighborhood of 1 and strictly convex at 1. It is shown that a slight modification of the disparity statistics introduced for testing the goodness-of-fit in 1986 by Hall are the phi-disparity statistics D-n(phi) = nD(phi) ((q) over bar (n), p(n)). These modified statistics can be ordered for 1 less than or equal to m less than or equal to n as to their sensitivity to alternatives. The limit laws governing for n --> infinity the distributions of the statistics under local alternatives are shown to be unchanged by the modification, which allows to construct the asymptotically a-level goodness-of-fit tests based on D-n(phi). In spite of that the limit laws depend only on the local properties of phi in a neighborhood of 1, we show by a simulation that for small and medium sample sizes n the true test sizes and powers significantly depend on phi and also on the alternatives, so that an adaptation of phi to concrete situations can improve performance of the phi-disparity test. Relations of D-n(phi) to some other m-spacing statistics known from the literature are discussed as well.