'Bootstrap sample' diaporamas de présentation

Building Conceptual Understanding of Statistical Inference

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Teaching with Simulation-Based Inference, for Beginners

Teaching with Simulation-Based Inference, for Beginners. Robin H Lock, St. Lawrence University Patti Frazer Lock, St. Lawrence University Kari Lock Morgan, Penn State University USCOTS May 2019. The Lock 5 Team. Kari [Harvard] Penn State. Eric [North Carolina] Minnesota. Dennis

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Confidence Intervals: Bootstrap Distribution

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Building Conceptual Understanding of Statistical Inference

Building Conceptual Understanding of Statistical Inference. Patti Frazer Lock Cummings Professor of Mathematics St. Lawrence University Canton, New York AMATYC November, 2013. The Lock 5 Team. Robin & Patti St. Lawrence. Dennis Iowa State. Eric UNC/Duke. Kari Harvard/Duke.

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Molecular Evolution and Phylogeny

Molecular Evolution and Phylogeny. Examples. Weakly deleterious mutations. Weakly deleterious mutations can reach high frequencies in local populations and, thus, may contribute significantly to genetic variance in disease susceptibility. Sequencing of human polymorphisms.

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Classification Ensemble Methods 1

Classification Ensemble Methods 1. Ensemble methods. Basic idea of ensemble methods: Combining predictions from competing models often gives better predictive accuracy than individual models. Shown to be empirically successful in wide variety of applications. See table on p. 294 of textbook.

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Case Selection and Resampling

Case Selection and Resampling. Lucila Ohno-Machado HST951. Topics. Case selection (influence detection) Regression diagnostics Sampling procedures Bootstrap Jackknife Cross-validation. Unusual Data. Outlier (discrepancy, unusual observation that may change parameters)

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A Fiddler on the Roof: Tradition vs. Modern Methods in Teaching Inference

A Fiddler on the Roof: Tradition vs. Modern Methods in Teaching Inference. Patti Frazer Lock Robin H. Lock St. Lawrence University Joint Mathematics Meetings January 2013. Simulation methods provide an exciting new method for teaching statistical inference!.

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Simulation and Resampling Methods in Introductory Statistics

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Hypothesis testing and confidence intervals by resampling

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David Speights Senior Research Statistician HNC Insurance Solutions Irvine, California

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YKL REA Northern Pike Model

YKL REA Northern Pike Model. Photo: ADF&G. Fish Distribution Models. Process AFFID data for use in models. Classification tree and random forest models. ADF&G AFFID species occurrence data. Create stream network and landscape predictor variables in GIS. GIS source data.

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Quantifying Uncertainty

Quantifying Uncertainty. Two approaches Use statistical theory Bootstrapping. Statistical Theory. Uncorrelated. Correlated. Effective sample size. Significance Statistics. Significance statistics use the standard error: Confidence Intervals:. Bootstrapping.

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Quantifying uncertainty using the bootstrap

Quantifying uncertainty using the bootstrap. Reading Efron, B. and R. Tibishirani, (1993), An Introduction to the Bootstrap , Chapman Hall, New York, 436 p. Chapters 1, 2, 6. Approaches to uncertainty estimation. Use statistical theory Bootstrapping. e.g. Standard Error.

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Examples of Ensemble Methods

Examples of Ensemble Methods. How to generate an ensemble of classifiers? Bagging Boosting. Bagging. Sampling with replacement Build a classifier on each bootstrap sample Each example has probability 1/N being selected

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3 pivot quantities on which to base bootstrap confidence intervals

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CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS

Bootstrapping Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis Technical University of Denmark (DTU). CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS. CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS.

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