Showing papers in "Journal of the Iranian Statistical Society in 2002"

Modeling Nonnegative Data with Clumping at Zero: A Survey

Abstract: Applications in which data take nonnegative values but have a substantial proportion of values at zero occur in many dis- ciplines. The modeling of such "clumped-at-zero" or "zero-inflated" data is challenging. We survey models that have been proposed. We consider cases in which the response for the non-zero observations is continuous and in which it is discrete. For the continuous and then the discrete case, we review models for analyzing cross-sectional data. We then summarize extensions for repeated measurement analyses (e.g., in longitudinal studies), for which the literature is still sparse. We also mention applications in which more than one clump can oc- cur and we suggest problems for future research.

Parametric and Nonparametric Regression with Missing X’s—A Review

Abstract: This paper gives a detailed overview of the problem of missing data in parametric and nonparametric regression. Theoreti- cal basics, properties as well as simulation results may help the reader to get familiar with the common problem of incomplete data sets. Of course, not all occurences can be discussed so this paper could be seen as an introduction to missing data within regression analysis and as an extension to the early paper of (19).

On efficiency criteria in density estimation

Abstract: We discuss the classical eciency criteria in density esti- mation and propose some variants. The context is a general density estimation scheme that contains the cases of i.i.d. or dependent ran- dom variables, in discrete or continuous time. Unbiased estimation, optimality and asymptotic optimality are considered. An example of a density estimator that satisfies some suggested criteria is given.

Bayesian Nonparametric and Parametric Inference

Abstract: This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Location Reparameterization and Default Priors for Statistical Analysis

Abstract: This paper develops default priors for Bayesian analysis that reproduce familiar frequentist and Bayesian analyses for models that are exponential or location. For the vector parameter case there is an information adjustment that avoids the Bayesian marginaliza- tion paradoxes and properly targets the prior on the parameter of interest thus adjusting for any complicating nonlinearity; the details of this vector Bayesian issue will be investigated in detail elsewhere. As in wide generality a statistical model has an inference component structure that is approximately exponential or approximately loca- tion to third order, this provides general default prior procedures that can be described as reweighting likelihood in accord with a Jereys' prior based on observed information. Two asymptotic models, that have variable and parameter of the same dimension and agree at a data point to first derivative con- ditional on an approximate ancillary, produce the same p-values to third order for inferences concerning scalar interest parameters. With