Journal of Computational & Graphical Statistics articles from December 2001

Journal of Computational & Graphical Statistics archives from December 2001

A Bayesian sampling approach to regression model checking.(Statistical Data Included)
December 1, 2001... A necessary step in any regression analysis is checking the fit of the model to the data. Graphical methods are often employed to allow visualization of features that the data should exhibit if the model holds. Judging whether such features are...

Generalized odds ratios for visual modeling.(Statistical Data Included)
December 1, 2001... The problem of displaying interaction effects graphically has been studied for a long time. This article shows how mosaic plots can be applied to visualize interaction effects in categorical data. This idea leads to a graphical approach for...

Generalized nonparametric mixed effects models.(Statistical Data Included)
December 1, 2001... Generalized linear mixed effects models (GLMM) provide useful tools for correlated and/or over-dispersed non-Gaussian data. This article considers generalized nonparametric mixed effects models (GNMM), which relax the rigid linear assumption on...

Analyzing the sensitivity of generalized linear models to incomplete outcomes via the IDE algorithm.(Statistical Data Included)
December 1, 2001... Incomplete data models typically involve strong untestable assumptions about the missing data distribution. As inference may critically depend on them, the importance of sensitivity analysis is well recognized. Molenberghs, Kenward, and...

An analysis of the role of positivity and mixture model constraints in poisson deconvolution problems.(Statistical Data Included)
December 1, 2001... We consider a class of estimation problems in which data of a Poisson character are related by a linear model to a target function that satisfies certain physical constraints. The classic example of this situation is the reconstruction problem...

A component-wise EM algorithm for mixtures.(Statistical Data Included)
December 1, 2001... Maximum likelihood estimation in finite mixture distributions is typically approached as an incomplete data problem to allow application of the expectation-maximization (EM) algorithm. In its general formulation, the EM algorithm involves the...

CriSP: a tool for bump hunting.(Statistical Data Included)
December 1, 2001... We propose a test of multimodality of regression functions and their derivatives. The test statistic is a critical smoothing parameter (CriSP), giving the minimum amount of smoothing necessary to force the regression function to satisfy the...

A Markov chain Monte Carlo algorithm for approximating exact conditional probabilities.(Statistical Data Included)
December 1, 2001... Conditional inference eliminates nuisance parameters by conditioning on their sufficient statistics. For contingency tables conditional inference entails enumerating all tables with the same sufficient statistics as the observed data. For...

The orthogonally constrained regression revisited.(Statistical Data Included)
December 1, 2001... The Penrose regression problem, including the orthonormal Procrustes problem and rotation problem to a partially specified target, is an important class of data matching problems arising frequently in multivariate analysis, yet its optimality...

Bayesian binary segmentation procedure for a Poisson process with multiple changepoints.(Statistical Data Included)
December 1, 2001... We observe n events occurring in (0, T] taken from a Poisson process. The intensity function of the process is assumed to be a step function with multiple changepoints. This article proposes a Bayesian binary segmentation procedure for locating...

Data sharpening for nonparametric inference subject to constraints.(Statistical Data Included)
December 1, 2001... Data sharpening involves perturbing the data to improve the performance of a statistical method. The versions of it that have been proposed in the past have been for bias reduction in curve estimation, and the amount of perturbation of each...