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Physical Sciences and Mathematics Commons™
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- Ackley function; evolutionary computation; multiple hypothesis testing; optimization; performance comparison; time series (1)
- Bonferroni; confidence region; discrete survival curve; Multiple Sclerosis; normal bound (1)
- Cross-validation; evolutionary algorithms; loss-based estimation; machine learning; optimization; parameter space (1)
- Genetics (1)
- Literate programming; R; Environmental epidemiology; Sweave (1)
Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Bayesian Analysis For Penalized Spline Regression Using Win Bugs, Ciprian M. Crainiceanu, David Ruppert, M.P. Wand
Bayesian Analysis For Penalized Spline Regression Using Win Bugs, Ciprian M. Crainiceanu, David Ruppert, M.P. Wand
Johns Hopkins University, Dept. of Biostatistics Working Papers
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. MCMC mixing is substantially improved from the previous versions by using low{rank thin{plate splines instead of truncated polynomial basis. Simulation time …
Loss-Based Estimation With Evolutionary Algorithms And Cross-Validation, David Shilane, Richard H. Liang, Sandrine Dudoit
Loss-Based Estimation With Evolutionary Algorithms And Cross-Validation, David Shilane, Richard H. Liang, Sandrine Dudoit
U.C. Berkeley Division of Biostatistics Working Paper Series
Many statistical inference methods rely upon selection procedures to estimate a parameter of the joint distribution of explanatory and outcome data, such as the regression function. Within the general framework for loss-based estimation of Dudoit and van der Laan, this project proposes an evolutionary algorithm (EA) as a procedure for risk optimization. We also analyze the size of the parameter space for polynomial regression under an interaction constraints along with constraints on either the polynomial or variable degree.
Time-Dependent Performance Comparison Of Stochastic Optimization Algorithms, David Shilane, Jarno Martikainen, Seppo Ovaska
Time-Dependent Performance Comparison Of Stochastic Optimization Algorithms, David Shilane, Jarno Martikainen, Seppo Ovaska
U.C. Berkeley Division of Biostatistics Working Paper Series
This paper proposes a statistical methodology for comparing the performance of stochastic optimization algorithms that iteratively generate candidate optima. The fundamental data structure of the results of these algorithms is a time series. Algorithmic differences may be assessed through a procedure of statistical sampling and multiple hypothesis testing of time series data. Shilane et al. propose a general framework for performance comparison of stochastic optimization algorithms that result in a single candidate optimum. This project seeks to extend this framework to assess performance in time series data structures. The proposed methodology analyzes empirical data to determine the generation intervals in …
Survival Analysis With Large Dimensional Covariates: An Application In Microarray Studies, David A. Engler, Yi Li
Survival Analysis With Large Dimensional Covariates: An Application In Microarray Studies, David A. Engler, Yi Li
Harvard University Biostatistics Working Paper Series
Use of microarray technology often leads to high-dimensional and low- sample size data settings. Over the past several years, a variety of novel approaches have been proposed for variable selection in this context. However, only a small number of these have been adapted for time-to-event data where censoring is present. Among standard variable selection methods shown both to have good predictive accuracy and to be computationally efficient is the elastic net penalization approach. In this paper, adaptation of the elastic net approach is presented for variable selection both under the Cox proportional hazards model and under an accelerated failure time …
Simultaneous Confidence Intervals Based On The Percentile Bootstrap Approach, Micha Mandel, Rebecca A. Betensky
Simultaneous Confidence Intervals Based On The Percentile Bootstrap Approach, Micha Mandel, Rebecca A. Betensky
Harvard University Biostatistics Working Paper Series
No abstract provided.
Distributed Reproducible Research Using Cached Computations, Roger Peng, Sandrah P. Eckel
Distributed Reproducible Research Using Cached Computations, Roger Peng, Sandrah P. Eckel
Johns Hopkins University, Dept. of Biostatistics Working Papers
The ability to make scientific findings reproducible is increasingly important in areas where substantive results are the product of complex statistical computations. Reproducibility can allow others to verify the published findings and conduct alternate analyses of the same data. A question that arises naturally is how can one conduct and distribute reproducible research? This question is relevant from the point of view of both the authors who want to make their research reproducible and readers who want to reproduce relevant findings reported in the scientific literature. We present a framework in which reproducible research can be conducted and distributed via …
A Reproducible Research Toolkit For R, Roger Peng
A Reproducible Research Toolkit For R, Roger Peng
Johns Hopkins University, Dept. of Biostatistics Working Papers
We present a collection of R packages for conducting and distributing reproducible research using R, Sweave, and LaTeX. The collection consists of the cacheSweave, stashR, and SRPM packages which allow for the caching of computations in Sweave documents and the distribution of those cached computations via remotely accessible key-value databases. We describe the caching mechanism used by the cacheSweave package and tools that we have developed for authors and readers for the purposes of creating and interacting with reproducible documents.