Resampling

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

On Generating Monte Carlo Samples of Continuous Diffusion Bridges

Abstract: Diffusion processes are widely used in engineering, finance, physics, and other fields. Usually continuous-time diffusion processes can be observed only at discrete time points. For many applications, it is often useful to impute continuous-time bridge samples that follow the diffusion dynamics and connect each pair of the consecutive observations. The sequential Monte Carlo (SMC) method is a useful tool for generating the intermediate paths of the bridge. The paths often are generated forward from the starting observation and forced in some ways to connect with the end observation. In this article we propose a constrained SMC algorithm with an effective resampling scheme guided by backward pilots carrying the information of the end observation. This resampling scheme can be easily combined with any forward SMC sampler. Two synthetic examples are used to demonstrate the effectiveness of the resampling scheme.

Simulation: A Modeler's Approach

Abstract: The generation of "random" numbers random quadrature Monte Carlo solutions of differential equations Markov chains, Poisson processes and linear equations SIMEST, SIMDAT, and pseudoreality models for stocks and derivatives simulation assessment of multivariate and robust procedures in statistical process control noise and chaos Bayesian approaches resampling based tests optimisation and estimation in a noisy world modeling the USA AIDS epidemic - exploration, simulation and conjecture.

Permutation Testing in the Presence of Polygenic Variation

Abstract: This article discusses problems with and solutions to performing valid permutation tests for quantitative trait loci in the presence of polygenic effects. Although permutation testing is a popular approach for determining statistical significance of a test statistic with an unknown distribution--for instance, the maximum of multiple correlated statistics or some omnibus test statistic for a gene, gene-set, or pathway--naive application of permutations may result in an invalid test. The risk of performing an invalid permutation test is particularly acute in complex trait mapping where polygenicity may combine with a structured population resulting from the presence of families, cryptic relatedness, admixture, or population stratification. I give both analytical derivations and a conceptual understanding of why typical permutation procedures fail and suggest an alternative permutation-based algorithm, MVNpermute, that succeeds. In particular, I examine the case where a linear mixed model is used to analyze a quantitative trait and show that both phenotype and genotype permutations may result in an invalid permutation test. I provide a formula that predicts the amount of inflation of the type 1 error rate depending on the degree of misspecification of the covariance structure of the polygenic effect and the heritability of the trait. I validate this formula by doing simulations, showing that the permutation distribution matches the theoretical expectation, and that my suggested permutation-based test obtains the correct null distribution. Finally, I discuss situations where naive permutations of the phenotype or genotype are valid and the applicability of the results to other test statistics.

Evaluation of Classifier Models Using Stratified Tenfold Cross Validation Techniques

Abstract: One of the important datamining function is prediction. Many predictive models can be built for the data. The data may be continous, categorical or combination of both. For either of the above type of data many similar predictive models are available. So it is highly important to choose the possible best accurate predictive model for the user data . For this the models are evaluated using resampling techniques. The evaluated models gives statistical results respectively. These statistical results are analysed and compared . The appropriate model that gives maximum accuracy for the user data is used to do predictions for further data of same type. The predictions thus made by the suitable model can be visualized which forms the decision reports for the user data. A proposal is made to apply fuzzy rough set techniques for evaluation of classifier models [7].

Resampling, Validating, Describing, and Simplifying the Model

Abstract: When one assumes that a random variable Y has a certain population distribution, one can use simulation or analytic derivations to study how a statistical estimator computed from samples from this distribution behaves. For example, when Y has a log-normal distribution, the variance of the sample median for a sample of size n from that distribution can be derived analytically. Alternatively, one can simulate 500 samples of size n from the log-normal distribution, compute the sample median for each sample, and then compute the sample variance of the 500 sample medians. Either case requires knowledge of the population distribution function.