1,721,025 research outputs found
A Bayesian adjustment of the modified profile likelihood
No full textWe propose an adjustment of the modified profile likelihood based on a suitable matching prior on the parameter of interest only, i.e. a prior for which there is an agreement between frequentist and Bayesian inference.We show that the proposed modified profile likelihood has several desiderable inferential
properties. Two examples are illustrated
Higher-order asymptotics in Bayesian inference
No full textThis paper reviews recent developments in higher-order asymptotics for
marginal posterior distributions, and related quantities, for practical use in Bayesian
analysis. In this respect, we outline how modern asymptotic theory, which provides
accurate inferences in a variety of parametric statistical problems even for small
sample sizes, may routinely be applied in practice. The focus is on default Bayesian
inference in the presence of nuisance parameter
On interval and point estimators based on a penalization of the modified profile likelihood
Full text linkVarious modifications of the profile likelihood have been proposed in the literature. Despite modified profile likelihood methods have better properties than those based on the profile likelihood, the signed likelihood ratio statistic based on the modified profile likelihood has a standard normal distribution only to first order, and it can be inaccurate in particular in models with many nuisance parameters. In this paper we propose an adjustment of the profile likelihood from a new perspective. The idea is to resort to suitable default priors on the parameter of interest only to be used as non-negative weight functions in order to modify the modified profile likelihood. In particular, we focus on matching priors, i.e. priors on the parameter of interest only for which there is an agreement between frequentist and Bayesian inference, derived from modified profile likelihoods. The proposed modified profile likelihood has desiderable inferential properties: the corresponding signed likelihood ratio statistic is standard normal to second order and the correponding maximizer is a refinement of the maximum likelihood estimator, which improves its small sample properties. Examples illustrate the proposed modified profile likelihood and outline its improvement over its counterparts
Uncertainty in statistical matching for complex sample surveys
No full textAn important feature of statistical matching is the estimation of the underlying joint distribution of variables separately available from independent sample surveys. Unless special assumptions are made, the absence of joint information on the variables of interest leads to uncertainty about the data generating model. The aim of this paper is to analyze the uncertainty in statistical matching for complex survey data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Bayesian nonparametric model for density and cluster estimation: the epsilon-NGG process mixture.
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