Resampling

About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.

Adaptive web sampling.

Abstract: A flexible class of adaptive sampling designs is introduced for sampling in network and spatial settings. In the designs, selections are made sequentially with a mixture distribution based on an active set that changes as the sampling progresses, using network or spatial relationships as well as sample values. The new designs have certain advantages compared with previously existing adaptive and link-tracing designs, including control over sample sizes and of the proportion of effort allocated to adaptive selections. Efficient inference involves averaging over sample paths consistent with the minimal sufficient statistic. A Markov chain resampling method makes the inference computationally feasible. The designs are evaluated in network and spatial settings using two empirical populations: a hidden human population at high risk for HIV/AIDS and an unevenly distributed bird population.

Design and analysis of quality of life studies in clinical trials : interdisciplinary statistics

Abstract: INTRODUCTION Health-Related Quality of Life Measuring Health-Related Quality of Life Example 1: Adjuvant Breast Cancer Trial Example 2: Advanced Non-Small-Cell Lung Cancer (NSCLC) Example 3: Renal Cell Carcinoma Trial Summary STUDY DESIGN AND PROTOCOL DEVELOPMENT Introduction Background and Rationale Research Objectives Selection of Subjects Longitudinal Designs Selection of a Quality of Life Measure Conduct Summary MODELS FOR LONGITUDINAL STUDIES Introduction Building the Analytic Models Building Repeated Measures Models Building Growth Curve Models Summary MISSING DATA Introduction Patterns of Missing Data Mechanisms of Missing Data Summary ANALYTIC METHODS FOR IGNORABLE MISSING DATA Introduction Repeated Univariate Analyses Multivariate Methods Baseline Assessment as a Covariate Change from Baseline Empirical Bayes Estimates Summary SIMPLE IMPUTATION Introduction Mean Value Substitution Explicit Regression Models Last Value Carried Forward Underestimation of Variance Sensitivity Analysis Summary MULTIPLE IMPUTATION Introduction Overview of Multiple Imputation Explicit Univariate Regression Closest Neighbor and Predictive Mean Matching Approximate Bayesian Bootstrap Multivariate Procedures for Nonmonotone Missing Data Combining the M Analyses Sensitivity Analyses Imputation vs. Analytic Models Implications for Design Summary PATTERN MIXTURE MODELS Introduction Bivariate Data (Two Repeated Measures) Monotone Dropout Parametric Models Additional Reading Algebraic Details Summary RANDOM-EFFECTS MIXTURE, SHARED-PARAMETER, AND SELECTION MODELS Introduction Conditional Linear Model Joint Mixed-Effects and Time to Dropout Selection Model for Monotone Dropout Advanced Readings Summary SUMMARY MEASURES Introduction Choosing a Summary Measure Constructing Summary Measures Summary Statistics across Time Summarizing Across HRQoL Domains or Subscales Advanced Notes Summary MULTIPLE ENDPOINTS Introduction Background Concepts and Definitions Multivariate Statistics Univariate Statistics Resampling Techniques Summary DESIGN: ANALYSIS PLANS Introduction General Analysis Plan Models for Longitudinal Data Multiplicity of Endpoints Sample Size and Power Reported Results Summary APPENDICES BIBLIOGRAPHY

Using Monte-Carlo variance reduction in statistical tolerance synthesis

Abstract: A statistical tolerance synthesis must analyse many sets of tolerances, each of which has a unique probability distribution. The Monte-Carlo technique that is typically used to evaluate the probability distribution must analyse large numbers of individual cases. The result is a huge number of individual analyses, which is computationally expensive. This paper examines two Monte-Carlo variance reduction techniques, importance sampling and correlation, and proposes a method for using them in statistical tolerance synthesis. Correlation is used to reduce the error in the tolerance analyses. Importance sampling is used to estimate the sensitivity of an analysis to the tolerances so that a gradient based optimization algorithm can be used.

Accounting for variability in the use of permutation testing to detect quantitative trait loci.

Abstract: Locating quantitative trait loci (QTL), or genomic regions associated with known molecular markers, is of increasing interest in a wide variety of applications ranging from human genetics to agricultural genetics. The hope of locating QTL (or genes) affecting a quantitative trait is that it will lead to characterization and possible manipulations of these genes. However, the complexity of both statistical and genetic issues surrounding the location of these regions calls into question the asymptotic statistical results supplying the distribution of the test statistics employed. Coupled with the power of current-day computing, permutation theory was reintroduced for the purpose of estimating the distribution of any test statistic used to test for the location of QTL. Permutation techniques have offered an attractive alternative to significance measures based on asymptotic theory. The ideas of permutation testing are extended in this application to include confidence intervals for the thresholds and p-values estimated in permutation testing procedures. The confidence intervals developed account for the Monte Carlo error associated with practical applications of permutation testing and lead to an effective method of determining an efficient permutation sample size.