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Confidence Interval Coverage (confidence + interval_coverage)

Distribution by Scientific Domains
Mathematics and Statistics75%

Selected Abstracts

Simultaneous localization of two linked disease susceptibility genes

GENETIC EPIDEMIOLOGY, Issue 1 2005
Joanna M. Biernacka
Abstract For diseases with complex genetic etiology, more than one susceptibility gene may exist in a single chromosomal region. Extending the work of Liang et al. ([2001] Hum. Hered. 51:64,78), we developed a method for simultaneous localization of two susceptibility genes in one region. We derived an expression for expected allele sharing of an affected sib pair (ASP) at each point across a chromosomal segment containing two susceptibility genes. Using generalized estimating equations (GEE), we developed an algorithm that uses marker identical-by-descent (IBD) sharing in affected sib pairs to simultaneously estimate the locations of the two genes and the mean IBD sharing in ASPs at these two disease loci. Confidence intervals for gene locations can be constructed based on large sample approximations. Application of the described methods to data from a genome scan for type 1 diabetes (Mein et al. [1998] Nat. Genet. 19:297,300) yielded estimates of two putative disease gene locations on chromosome 6, approximately 20 cM apart. Properties of the estimators, including bias, precision, and confidence interval coverage, were studied by simulation for a range of genetic models. The simulations demonstrated that the proposed method can improve disease gene localization and aid in resolving large peaks when two disease genes are present in one chromosomal region. Joint localization of two disease genes improves with increased excess allele sharing at the disease gene loci, increased distance between the disease genes, and increased number of affected sib pairs in the sample. Genet. Epidemiol. © 2004 Wiley-Liss, Inc. [source]

Complementary Log,Log Regression for the Estimation of Covariate-Adjusted Prevalence Ratios in the Analysis of Data from Cross-Sectional Studies

BIOMETRICAL JOURNAL, Issue 3 2009
Alan D. Penman
Abstract We assessed complementary log,log (CLL) regression as an alternative statistical model for estimating multivariable-adjusted prevalence ratios (PR) and their confidence intervals. Using the delta method, we derived an expression for approximating the variance of the PR estimated using CLL regression. Then, using simulated data, we examined the performance of CLL regression in terms of the accuracy of the PR estimates, the width of the confidence intervals, and the empirical coverage probability, and compared it with results obtained from log,binomial regression and stratified Mantel,Haenszel analysis. Within the range of values of our simulated data, CLL regression performed well, with only slight bias of point estimates of the PR and good confidence interval coverage. In addition, and importantly, the computational algorithm did not have the convergence problems occasionally exhibited by log,binomial regression. The technique is easy to implement in SAS (SAS Institute, Cary, NC), and it does not have the theoretical and practical issues associated with competing approaches. CLL regression is an alternative method of binomial regression that warrants further assessment. [source]

Estimating Abundance Using Mark,Resight When Sampling Is with Replacement or the Number of Marked Individuals Is Unknown

BIOMETRICS, Issue 1 2009
Brett T. McClintock
Summary Although mark,resight methods can often be a less expensive and less invasive means for estimating abundance in long-term population monitoring programs, two major limitations of the estimators are that they typically require sampling without replacement and/or the number of marked individuals available for resighting to be known exactly. These requirements can often be difficult to achieve. Here we address these limitations by introducing the Poisson log and zero-truncated Poisson log-normal mixed effects models (PNE and ZPNE, respectively). The generalized framework of the models allow the efficient use of covariates in modeling resighting rate and individual heterogeneity parameters, information-theoretic model selection and multimodel inference, and the incorporation of individually unidentified marks. Both models may be implemented using standard statistical computing software, but they have also been added to the mark,recapture freeware package Program MARK. We demonstrate the use and advantages of (Z)PNE using black-tailed prairie dog data recently collected in Colorado. We also investigate the expected relative performance of the models in simulation experiments. Compared to other available estimators, we generally found (Z)PNE to be more precise with little or no loss in confidence interval coverage. With the recent introduction of the logit-normal mixed effects model and (Z)PNE, a more flexible and efficient framework for mark,resight abundance estimation is now available for the sampling conditions most commonly encountered in these studies. [source]

Replicated batch means for steady-state simulations

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 6 2006
Nilay Tan, k Argon
Abstract This paper studies a new steady-state simulation output analysis method called replicated batch means in which a small number of replications are conducted and the observations in these replications are grouped into batches. This paper also introduces and compares methods for selecting the initial state of each replication. More specifically, we show that confidence intervals constructed by the replicated batch means method are valid for large batch sizes and derive expressions for the expected values and variances of the steady-state mean and variance estimators for stationary processes and large sample sizes. We then use these expressions, analytical examples, and numerical experiments to compare the replicated batch means method with the standard batch means and multiple replications methods. The numerical results, which are obtained from an AR(1) process and a small, nearly-decomposable Markov chain, show that the multiple replications method often gives confidence intervals with poorer coverage than the standard and replicated batch means methods and that the replicated batch means method, implemented with good choices of initialization method and number of replications, provides confidence interval coverages that range from being comparable with to being noticeably better than coverages obtained by the standard batch means method. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006 [source]