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Independence Assumption (independence + assumption)

Distribution by Scientific Domains

Mathematics and Statistics	57%

Selected Abstracts

From temporary help jobs to permanent employment: what can we learn from matching estimators and their sensitivity?

JOURNAL OF APPLIED ECONOMETRICS, Issue 3 2008
Andrea Ichino
The diffusion of temporary work agency (TWA) jobs has led to a harsh policy debate and ambiguous empirical evidence. Results for the USA, based on quasi-experimental evidence, suggest that a TWA assignment decreases the probability of finding a stable job, while results for Europe, based on the conditional independence assumption (CIA), typically reach opposite conclusions. Using data for two Italian regions, we rely on a matching estimator to show that TWA assignments can be an effective springboard to permanent employment. We also propose a simulation-based sensitivity analysis, which highlights that only for one of these two regions are our results robust to specific failures of the CIA. We conclude that European studies based on the CIA should not be automatically discarded, but should be put under the scrutiny of a sensitivity analysis like the one we propose. Copyright © 2008 John Wiley & Sons, Ltd. [source]

The funnel experiment: The Markov-based SPC approach

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2007
Gonen Singer
Abstract The classical funnel experiment was used by Deming to promote the idea of statistical process control (SPC). The popular example illustrates that the implementation of simple feedback rules to stationary processes violates the independence assumption and prevents the implementation of conventional SPC. However, Deming did not indicate how to implement SPC in the presence of such feedback rules. This pedagogical gap is addressed here by introducing a simple feedback rule to the funnel example that results in a nonlinear process to which the traditional SPC methods cannot be applied. The proposed method of Markov-based SPC, which is a simplified version of the context-based SPC method, is shown to monitor the modified process well. Copyright © 2007 John Wiley & Sons, Ltd. [source]

An Independence Test for Doubly Censored Failure Time Data

BIOMETRICAL JOURNAL, Issue 5 2004
Jianguo Sun
Abstract The analysis of doubly censored failure time data has recently attracted a great deal of attention and for this, a number of methods have been proposed (De Gruttola and Lagakos, 1989; Kim et al., 1993; Pan, 2001; Sun, 2004). To simplify the analysis, most of these methods make an independence assumption: the distribution of the survival time of interest is independent of the occurrence of the initial event that defines the survival time. Although it is well-known that the assumption may not be true, there does not seem to be any existing research discussing the checking of the assumption. In this article, a Wald test is developed for testing this assumption and the method is applied to an AIDS cohort study. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Mixed-Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout

BIOMETRICS, Issue 2 2009
Ying Yuan
Summary Selection models and pattern-mixture models are often used to deal with nonignorable dropout in longitudinal studies. These two classes of models are based on different factorizations of the joint distribution of the outcome process and the dropout process. We consider a new class of models, called mixed-effect hybrid models (MEHMs), where the joint distribution of the outcome process and dropout process is factorized into the marginal distribution of random effects, the dropout process conditional on random effects, and the outcome process conditional on dropout patterns and random effects. MEHMs combine features of selection models and pattern-mixture models: they directly model the missingness process as in selection models, and enjoy the computational simplicity of pattern-mixture models. The MEHM provides a generalization of shared-parameter models (SPMs) by relaxing the conditional independence assumption between the measurement process and the dropout process given random effects. Because SPMs are nested within MEHMs, likelihood ratio tests can be constructed to evaluate the conditional independence assumption of SPMs. We use data from a pediatric AIDS clinical trial to illustrate the models. [source]

Exploiting Gene-Environment Independence for Analysis of Case,Control Studies: An Empirical Bayes-Type Shrinkage Estimator to Trade-Off between Bias and Efficiency

BIOMETRICS, Issue 3 2008
Bhramar Mukherjee
Summary Standard prospective logistic regression analysis of case,control data often leads to very imprecise estimates of gene-environment interactions due to small numbers of cases or controls in cells of crossing genotype and exposure. In contrast, under the assumption of gene-environment independence, modern "retrospective" methods, including the "case-only" approach, can estimate the interaction parameters much more precisely, but they can be seriously biased when the underlying assumption of gene-environment independence is violated. In this article, we propose a novel empirical Bayes-type shrinkage estimator to analyze case,control data that can relax the gene-environment independence assumption in a data-adaptive fashion. In the special case, involving a binary gene and a binary exposure, the method leads to an estimator of the interaction log odds ratio parameter in a simple closed form that corresponds to an weighted average of the standard case-only and case,control estimators. We also describe a general approach for deriving the new shrinkage estimator and its variance within the retrospective maximum-likelihood framework developed by Chatterjee and Carroll (2005, Biometrika92, 399,418). Both simulated and real data examples suggest that the proposed estimator strikes a balance between bias and efficiency depending on the true nature of the gene-environment association and the sample size for a given study. [source]

Models for Estimating Bayes Factors with Applications to Phylogeny and Tests of Monophyly

BIOMETRICS, Issue 3 2005
Marc A. Suchard
Summary Bayes factors comparing two or more competing hypotheses are often estimated by constructing a Markov chain Monte Carlo (MCMC) sampler to explore the joint space of the hypotheses. To obtain efficient Bayes factor estimates, Carlin and Chib (1995, Journal of the Royal Statistical Society, Series B57, 473,484) suggest adjusting the prior odds of the competing hypotheses so that the posterior odds are approximately one, then estimating the Bayes factor by simple division. A byproduct is that one often produces several independent MCMC chains, only one of which is actually used for estimation. We extend this approach to incorporate output from multiple chains by proposing three statistical models. The first assumes independent sampler draws and models the hypothesis indicator function using logistic regression for various choices of the prior odds. The two more complex models relax the independence assumption by allowing for higher-lag dependence within the MCMC output. These models allow us to estimate the uncertainty in our Bayes factor calculation and to fully use several different MCMC chains even when the prior odds of the hypotheses vary from chain to chain. We apply these methods to calculate Bayes factors for tests of monophyly in two phylogenetic examples. The first example explores the relationship of an unknown pathogen to a set of known pathogens. Identification of the unknown's monophyletic relationship may affect antibiotic choice in a clinical setting. The second example focuses on HIV recombination detection. For potential clinical application, these types of analyses must be completed as efficiently as possible. [source]

Computation of Likelihood Ratios in Fingerprint Identification for Configurations of Any Number of Minutiæ

JOURNAL OF FORENSIC SCIENCES, Issue 1 2007
Cédric Neumann M.Sc.
ABSTRACT: Recent court challenges have highlighted the need for statistical research on fingerprint identification. This paper proposes a model for computing likelihood ratios (LRs) to assess the evidential value of comparisons with any number of minutiæ. The model considers minutiae type, direction and relative spatial relationships. It expands on previous work on three minutiae by adopting a spatial modeling using radial triangulation and a probabilistic distortion model for assessing the numerator of the LR. The model has been tested on a sample of 686 ulnar loops and 204 arches. Features vectors used for statistical analysis have been obtained following a preprocessing step based on Gabor filtering and image processing to extract minutiae data. The metric used to assess similarity between two feature vectors is based on an Euclidean distance measure. Tippett plots and rates of misleading evidence have been used as performance indicators of the model. The model has shown encouraging behavior with low rates of misleading evidence and a LR power of the model increasing significantly with the number of minutiæ. The LRs that it provides are highly indicative of identity of source on a significant proportion of cases, even when considering configurations with few minutiæ. In contrast with previous research, the model, in addition to minutia type and direction, incorporates spatial relationships of minutiæ without introducing probabilistic independence assumptions. The model also accounts for finger distortion. [source]