A Novel Particle Filter Aided by Interval Analysis Approach

This paper addresses the issue of state estimation in nonlinear systems in the presence of non-Gaussian and bounded noises, under which the interval analysis based estimation is introduced as an auxiliary approach of generic particle filter (PF). This yields the so-called Set-Membership aided particle filter (SMPF). Unlike the mature alternatives of generic particle filter, the proposal distribution of SMPF approximates the posterior probability density function (PDF), not only on the numerical value but also on the definition-domain, and the performance analysis on the proposed alternative is proven through detailed formulations. In addition, contrasting simulations under SMPF and other mature alternatives also validate the effectiveness of SMPF.     

A lower bound for the bayes risk in the sequential case

A lower bound for the Bayes risk in the sequential case is given under the regularity conditions. A related result to the minimax risk is also discussed. Further. some examples are given for the exponential and Poisson distributions.     

An On-Line Estimation Algorithm for Periodic Autoregressive Models

This paper develops an on-line estimation algorithm for periodic autoregressive models (PAR). Indeed, we provide an adaptation of the well known recursive least squares algorithm (RLS), which has been successfully applied to classical autoregressive models (AR), to deal with PAR models. The obtained estimators are shown to be asymptotically efficient under mild conditions. Moreover, the performance of the periodic least squares algorithm (PRLS) is assessed via an intensive simulation study.     

A Graphical Representation for Non-Parametric Inference

A method is described for determining the sample size required for a specified precision simultaneous confidence statement about the parameters of a multinomial population. The method is based on a simultaneous confidence interval procedure due to Goodman, and the results are compared with those obtained by separately considering each cell of the multinomial population as a binomial.     

Collective Labor Supply,Taxes, and Intrahousehold Allocation: An Empirical Approach

ABSTRACT

Most empirical studies of the impact of labor income taxation on the labor supply behavior of households use a unitary modeling approach. In this article, we empirically analyze income taxation and the choice of working hours by combining the collective approach for household behavior and the discrete hours choice framework with fixed costs of work. We identify the sharing rule parameters with data on working hours of both the husband and the wife within a couple. Parameter estimates are used to evaluate various model outcomes, like the wage elasticities of labor supply and the impacts of wage changes on the intrahousehold allocation of income. We also simulate the consequences of a policy change in the tax system. We find that the collective model has different empirical outcomes of income sharing than a restricted model that imposes income pooling. In particular, a specification with income pooling fails to capture asymmetries in the income sharing across spouses. These differences in outcomes have consequences for the evaluation of policy changes in the tax system and shed light on the effectiveness of certain policies.     

New ASA Fellows — 1964

This is an invited expository article for The American Statistician. It reviews the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule. The presentation is written at a relaxed mathematical level, omitting most proofs, regularity conditions, and technical details.     

Estimation of the binomial parameter,using an apriori hypothesis about its value

We study estimation of the parameter p of a r.v. x ～ Bin(p,n) using the prior hypothesis that p equals a prepecified value P_o when we entertain the possibility that P_o is not the right value of p, which then could be any value in (0.1). We apply notions of Hodges and Lehmann (1952), Bickel (1984) and Berger (1982) to obtain (biased) estimators which do well under the hypothesis that p = p_o at a small price in maximum risk. A number of examples and comparisons are discussed at the end of the paper.     

On the maximum likelihood estimation of parameters of a spatial discrimination model

Consideration is given here to the problem of maximum likelihood estimation of parameters in a sparial discrimination model which was proposed by switzer (1980). some moments of these estimators are derived. These results extend the work of Mardia (1984) who gave expressions for these estimators without their moments.     

Bayesian prediction for small areas using sur models

Sample surveys are usually designed and analyzed to produce estimates for larger areas and/or populations. Nevertheless, sample sizes are often not large enough to give adequate precision for small area estimates of interest. To circumvent such difficulties, borrowing strength from related small areas via modeling becomes essential. In line with this, we propose a hierarchical multivariate Bayes prediction method for small area estimation based on the seemingly unrelated regressions (SUR) model. The performance of the proposed method was evaluated through simulation studies.