PPT-Nonparametric and Resampling Statistics

Wilcoxon RankSum Test To compare two independent samples Null is that the two populations are identical The test statistic is W s Table of Critical Vals For large samples there is a normal approx. We propose a nonparametric di64256eomorphic image registra tion algorithm based on Thirions demons algorithm The dem ons algo rithm can be seen as an optimization procedure on the entire s pace of displacement 64257elds The main idea of our algorith isavectorofparameterstobeestimatedand x isavectorofpredictors forthe thof observationstheerrors areassumedtobenormallyandindependentlydistributedwith mean 0 and constant variance The function relating the average value of the response to the pred De64257nition A Bayesian nonparametric model is a Bayesian model on an in64257nitedimensional parameter space The parameter space is typically chosen as the set of all possi ble solutions for a given learning problem For example in a regression prob example Consider a short data set data 1 2 3 4 5 6 7 8 9 10 ans 1 2 3 4 5 6 7 8 9 10 excludedata5 ans 1 2 3 4 6 7 8 9 10 excludedata2 5 10 ans 1 3 4 6 7 8 9 If data is a matrix exclude works by rows x 1 2 3 4 5 6 7 8 ans 1 2 3 4 5 6 7 8 excludex2 Department of Electrical and Computer Engineering. Zhu Han. Department. of Electrical and Computer Engineering. University of Houston.. Thanks to Nam Nguyen. , . Guanbo. . Zheng. , and Dr. . Rong. . . Regression. COSC 878 Doctoral Seminar. Georgetown University. Presenters:. . Sicong Zhang. , . Jiyun. . Luo. .. April. . 1. 4. , 201. 5. 5.0. . Nonparametric Regression. 2. 5.0. . Nonparametric Regression. . A New Paradigm of Using Workload Data for Performance Evaluation. Dror . Feitelson. Hebrew University. Performance Evaluation. “Experimental computer science at its best” [Denning]. Major element of systems research. (Part 1). Allan Rossman, Cal Poly – San Luis Obispo. Robin Lock, St. Lawrence University. George Cobb (. TISE. , 2007). 2. “What . we teach is largely the technical machinery of numerical approximations based on the normal distribution and its many subsidiary cogs. This machinery was once necessary, because the conceptually simpler alternative based on permutations was computationally beyond our . Google Earth. Geometric Corrections. Rectification and Registration. Learning Objectives. Be able to define geometric correction.. Understand why geometric correction is usually necessary.. Understand the difference between . in Seismic Reservoir Modeling. Cheolkyun Jeong*, Tapan Mukerji, and Gregoire Mariethoz. Stanford Center for Reservoir Forecasting. How to quantify uncertainty of models? . Why quantify uncertainty?. 2. TAU Bootstrap Seminar 2011. Dr. Saharon Rosset. Shachar Kaufman. Based on Efron and Tibshirani’s . “An introduction to the bootstrap”. Chapter 14. Agenda. What’s wrong with the simpler intervals?. Try Analysis of Variance (ANOVA). Part IV. Significantly Different:. Using Inferential Statistics. What you will learn in Chapter 13. What Analysis of Variance (ANOVA) is and when it is appropriate to use. We have been primarily discussing parametric tests; i.e. , tests that hold certain assumptions about when they are valid, e.g. t-tests and ANOVA both had assumptions regarding the shape of the distribution (normality) and about the necessity of having similar groups (homogeneity of variance). . . conditional . VaR. . and . expected shortfall. Outline. Introduction. Nonparametric . Estimators. Statistical . Properties. Application. Introduction. Value-at-risk (. VaR. ) and expected shortfall (ES) are two popular measures of market risk associated with an asset or portfolio of assets..