Resampling Methods for Dependent Data
|
|
|
- Brooke Riley
- 5 years ago
- Views:
Transcription
1 S.N. Lahiri Resampling Methods for Dependent Data With 25 Illustrations Springer
2 Contents 1 Scope of Resampling Methods for Dependent Data The Bootstrap Principle Examples Concluding Remarks Notation 13 2 Bootstrap Methods * Introduction IID Bootstrap Inadequacy of IID Bootstrap for Dependent Data Bootstrap Based on IID Innovations Moving Block Bootstrap Nonoverlapping Block Bootstrap Generalized Block Bootstrap Circular Block Bootstrap Stationary Block Bootstrap Subsampling Transformation-Based Bootstrap Sieve Bootstrap 41 3 Properties of Block Bootstrap Methods for the Sample Mean Introduction Consistency of MBB, NBB, CBB: Sample Mean 47
3 XII Contents Consistency of Bootstrap Variance Estimators Consistency of Distribution Function Estimators Consistency of the SB: Sample Mean Consistency of SB Variance Estimators Consistency of SB Distribution Function Estimators 63 4 Extensions and Examples Introduction Smooth Functions of Means M-Estimators Differentiate Functional Bootstrapping the Empirical Process Consistency of the MBB for Differentiable Statistical Functionals Examples 99 5 Comparison of Block Bootstrap Methods Introduction Empirical Comparisons The Theoretical Framework Expansions for the MSEs Theoretical Comparisons Asymptotic Efficiency Comparison at Optimal Block Lengths Concluding Remarks Proofs Proofs of Theorems for the MBB, the NBB, and the CBB Proofs of Theorems for the SB Second-Order Properties Introduction Edgeworth Expansions for the Mean Under Independence Edgeworth Expansions for the Mean Under Dependence Expansions for Functions of Sample Means Expansions Under the Smooth Function Model Under Independence Expansions for Normalized and Studentized Statistics Under Independence 163^ Expansions for Normalized Statistics Under Dependence Expansions for Studentized Statistics Under Dependence Second-Order Properties of Block Bootstrap Methods
4 Contents XIII 7 Empirical Choice of the Block Size Introduction Theoretical Optimal Block Lengths Optimal Block Lengths for Bias and Variance Estimation Optimal Block Lengths for Distribution Function Estimation A Method Based on Subsampling A Nonparametric Plug-in Method Motivation The Bias Estimator The JAB Variance Estimator The Optimal Block Length Estimator Model-Based Bootstrap Introduction Bootstrapping Stationary Autoregressive Processes Bootstrapping Explosive Autoregressive Processes Bootstrapping Unstable Autoregressive Processes Bootstrapping a Stationary ARMA Process Frequency Domain Bootstrap Introduction Bootstrapping Ratio Statistics Spectral Means and Ratio Statistics Frequency Domain Bootstrap for Ratio Statistics Second-Order Correctness of the FDB Bootstrapping Spectral Density Estimators Frequency Domain Bootstrap for Spectral Density Estimation Consistency of the FDB Distribution Function Estimator Bandwidth Selection A Modified FDB Motivation The Autoregressive-Aided FDB A Long-Range Dependence Introduction A Class of Long-Range Dependent Processes Properties of the MBB Method Main Results Proofs Properties of the Subsampling Method Results on the Normalized Sample Mean 252
5 XIV Contents Results on the Studentized Sample Mean Proofs., Numerical Results Bootstrapping Heavy-Tailed Data and Extremes Introduction Heavy-Tailed Distributions Consistency of the MBB Invalidity of the MBB Extremes of Stationary Random Variables Results on Bootstrapping Extremes Bootstrapping Extremes With Estimated Constants Resampling Methods for Spatial Data Introduction Spatial Asymptotic Frameworks Block Bootstrap for Spatial Data on a Regular Grid Description of the Block Bootstrap Method Numerical Examples Consistency of Bootstrap Variance Estimators Results on the Empirical Distribution Function Differentiable Functional Estimation of Spatial Covariance Parameters The Variogram Least Squares Variogram Estimation The RGLS Method Properties of the RGLS Estimators Numerical Examples Bootstrap for Irregularly Spaced Spatial Data A Class of Spatial Stochastic Designs 319" Asymptotic Distribution of M-Estimators A Spatial Block Bootstrap Method Properties of the Spatial Bootstrap Method Resampling Methods for Spatial Prediction Prediction of Integrals Prediction of Point Values - % A 339 B 345 References 349 Author Index 367 Subject Index 371
COPYRIGHTED MATERIAL CONTENTS
PREFACE ACKNOWLEDGMENTS LIST OF TABLES xi xv xvii 1 INTRODUCTION 1 1.1 Historical Background 1 1.2 Definition and Relationship to the Delta Method and Other Resampling Methods 3 1.2.1 Jackknife 6 1.2.2
Stochastic Simulation: Algorithms and Analysis
Soren Asmussen Peter W. Glynn Stochastic Simulation: Algorithms and Analysis et Springer Contents Preface Notation v xii I What This Book Is About 1 1 An Illustrative Example: The Single-Server Queue 1
Nonparametric and Semiparametric Econometrics Lecture Notes for Econ 221. Yixiao Sun Department of Economics, University of California, San Diego
Nonparametric and Semiparametric Econometrics Lecture Notes for Econ 221 Yixiao Sun Department of Economics, University of California, San Diego Winter 2007 Contents Preface ix 1 Kernel Smoothing: Density
PATTERN CLASSIFICATION AND SCENE ANALYSIS
PATTERN CLASSIFICATION AND SCENE ANALYSIS RICHARD O. DUDA PETER E. HART Stanford Research Institute, Menlo Park, California A WILEY-INTERSCIENCE PUBLICATION JOHN WILEY & SONS New York Chichester Brisbane
An Introduction to the Bootstrap
An Introduction to the Bootstrap Bradley Efron Department of Statistics Stanford University and Robert J. Tibshirani Department of Preventative Medicine and Biostatistics and Department of Statistics,
Random Number Generation and Monte Carlo Methods
James E. Gentle Random Number Generation and Monte Carlo Methods With 30 Illustrations Springer Contents Preface vii 1 Simulating Random Numbers from a Uniform Distribution 1 1.1 Linear Congruential Generators
Chapter 3. Bootstrap. 3.1 Introduction. 3.2 The general idea
Chapter 3 Bootstrap 3.1 Introduction The estimation of parameters in probability distributions is a basic problem in statistics that one tends to encounter already during the very first course on the subject.
Time Series Analysis by State Space Methods
Time Series Analysis by State Space Methods Second Edition J. Durbin London School of Economics and Political Science and University College London S. J. Koopman Vrije Universiteit Amsterdam OXFORD UNIVERSITY
A selective bibliography on the Bootstrap
A selective bibliography on the Bootstrap P. Bertail CREST-Laboratoire de Statistiques 1 Books. The jackknife Gray, H., Schucany, W.(1972). The Generalized Jackknife Statistic. Marcel Dekker, New-York.
Analysis of Panel Data. Third Edition. Cheng Hsiao University of Southern California CAMBRIDGE UNIVERSITY PRESS
Analysis of Panel Data Third Edition Cheng Hsiao University of Southern California CAMBRIDGE UNIVERSITY PRESS Contents Preface to the ThirdEdition Preface to the Second Edition Preface to the First Edition
Statistics (STAT) Statistics (STAT) 1. Prerequisites: grade in C- or higher in STAT 1200 or STAT 1300 or STAT 1400
Statistics (STAT) 1 Statistics (STAT) STAT 1200: Introductory Statistical Reasoning Statistical concepts for critically evaluation quantitative information. Descriptive statistics, probability, estimation,
Institute for Statics und Dynamics of Structures Fuzzy Time Series
Institute for Statics und Dynamics of Structures Fuzzy Time Series Bernd Möller 1 Description of fuzzy time series 2 3 4 5 Conclusions Folie 2 von slide422 1 Description of fuzzy time series 2 3 4 5 Conclusions
The Bootstrap and Jackknife
The Bootstrap and Jackknife Summer 2017 Summer Institutes 249 Bootstrap & Jackknife Motivation In scientific research Interest often focuses upon the estimation of some unknown parameter, θ. The parameter
On the Test and Estimation of Fractional Parameter. in ARFIMA Model: Bootstrap Approach
Applied Mathematical Sciences, Vol. 8, 2014, no. 96, 4783-4792 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.46498 On the Test and Estimation of Fractional Parameter in ARFIMA Model:
Nonparametric Estimation of Distribution Function using Bezier Curve
Communications for Statistical Applications and Methods 2014, Vol. 21, No. 1, 105 114 DOI: http://dx.doi.org/10.5351/csam.2014.21.1.105 ISSN 2287-7843 Nonparametric Estimation of Distribution Function
Optimum Array Processing
Optimum Array Processing Part IV of Detection, Estimation, and Modulation Theory Harry L. Van Trees WILEY- INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION Preface xix 1 Introduction 1 1.1 Array Processing
Curve and Surface Fitting with Splines. PAUL DIERCKX Professor, Computer Science Department, Katholieke Universiteit Leuven, Belgium
Curve and Surface Fitting with Splines PAUL DIERCKX Professor, Computer Science Department, Katholieke Universiteit Leuven, Belgium CLARENDON PRESS OXFORD 1995 - Preface List of Figures List of Tables
MS&E 226: Small Data
MS&E 226: Small Data Lecture 13: The bootstrap (v3) Ramesh Johari ramesh.johari@stanford.edu 1 / 30 Resampling 2 / 30 Sampling distribution of a statistic For this lecture: There is a population model
DEPARTMENT OF STATISTICS
Department of Statistics 1 DEPARTMENT OF STATISTICS Office in Statistics Building, Room 102 (970) 491-5269 or (970) 491-6546 stat.colostate.edu (http://www.stat.colostate.edu) Don Estep, Department Chair
Ludwig Fahrmeir Gerhard Tute. Statistical odelling Based on Generalized Linear Model. íecond Edition. . Springer
Ludwig Fahrmeir Gerhard Tute Statistical odelling Based on Generalized Linear Model íecond Edition. Springer Preface to the Second Edition Preface to the First Edition List of Examples List of Figures
Conditional Volatility Estimation by. Conditional Quantile Autoregression
International Journal of Mathematical Analysis Vol. 8, 2014, no. 41, 2033-2046 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ijma.2014.47210 Conditional Volatility Estimation by Conditional Quantile
Overview. Spectral Processing of Point- Sampled Geometry. Introduction. Introduction. Fourier Transform. Fourier Transform
Overview Spectral Processing of Point- Sampled Geometry Introduction Fourier transform Spectral processing pipeline Spectral filtering Adaptive subsampling Summary Point-Based Computer Graphics Markus
Locally Weighted Least Squares Regression for Image Denoising, Reconstruction and Up-sampling
Locally Weighted Least Squares Regression for Image Denoising, Reconstruction and Up-sampling Moritz Baecher May 15, 29 1 Introduction Edge-preserving smoothing and super-resolution are classic and important
Big Data Methods. Chapter 5: Machine learning. Big Data Methods, Chapter 5, Slide 1
Big Data Methods Chapter 5: Machine learning Big Data Methods, Chapter 5, Slide 1 5.1 Introduction to machine learning What is machine learning? Concerned with the study and development of algorithms that
Adaptive System Identification and Signal Processing Algorithms
Adaptive System Identification and Signal Processing Algorithms edited by N. Kalouptsidis University of Athens S. Theodoridis University of Patras Prentice Hall New York London Toronto Sydney Tokyo Singapore
Section 4 Matching Estimator
Section 4 Matching Estimator Matching Estimators Key Idea: The matching method compares the outcomes of program participants with those of matched nonparticipants, where matches are chosen on the basis
Stat 510, Lab April 27, 2007
Stat 510, Lab 10.5 April 27, 2007 1 In Class 1.1 Smoothing Let s look at the Australian labour data which was seen in previous labs. We would like to estimate the spectral density of the data. We may generate
Overview of various smoothers
Chapter 2 Overview of various smoothers A scatter plot smoother is a tool for finding structure in a scatter plot: Figure 2.1: CD4 cell count since seroconversion for HIV infected men. CD4 counts vs Time
MOBILITY MODELS FOR NEXT WIRELESS NETWORKS GENERATION WILEY AD HOC, VEHICULAR AND MESH NETWORKS. Paolo Santi
MOBILITY MODELS FOR NEXT GENERATION WIRELESS NETWORKS AD HOC, VEHICULAR AND MESH NETWORKS Paolo Santi Istituto di Informatica e Telematica del CNR, Italy WILEY A John Wiley & Sons, Ltd., Publication Contents
Optimization and Simulation
Optimization and Simulation Statistical analysis and bootstrapping Michel Bierlaire Transport and Mobility Laboratory School of Architecture, Civil and Environmental Engineering Ecole Polytechnique Fédérale
On Kernel Density Estimation with Univariate Application. SILOKO, Israel Uzuazor
On Kernel Density Estimation with Univariate Application BY SILOKO, Israel Uzuazor Department of Mathematics/ICT, Edo University Iyamho, Edo State, Nigeria. A Seminar Presented at Faculty of Science, Edo
Finite Sample Criteria for Autoregressive Order Selection
3550 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 48, NO. 12, DECEMBER 2000 Finite Sample Criteria for Autoregressive Order Selection Piet M. T. Broersen Abstract The quality of selected AR models depends
Optimization in the Big Data Regime 5: Parallelization? Sham M. Kakade
Optimization in the Big Data Regime 5: Parallelization? Sham M. Kakade Machine Learning for Big Data CSE547/STAT548 University of Washington S. M. Kakade (UW) Optimization for Big data 1 / 21 Announcements...
Modelling and Quantitative Methods in Fisheries
SUB Hamburg A/553843 Modelling and Quantitative Methods in Fisheries Second Edition Malcolm Haddon ( r oc) CRC Press \ y* J Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of
Spatial Interpolation & Geostatistics
(Z i Z j ) 2 / 2 Spatial Interpolation & Geostatistics Lag Lag Mean Distance between pairs of points 1 Tobler s Law All places are related, but nearby places are related more than distant places Corollary:
Statistical Methods for the Analysis of Repeated Measurements
Charles S. Davis Statistical Methods for the Analysis of Repeated Measurements With 20 Illustrations #j Springer Contents Preface List of Tables List of Figures v xv xxiii 1 Introduction 1 1.1 Repeated
Last time... Coryn Bailer-Jones. check and if appropriate remove outliers, errors etc. linear regression
Machine learning, pattern recognition and statistical data modelling Lecture 3. Linear Methods (part 1) Coryn Bailer-Jones Last time... curse of dimensionality local methods quickly become nonlocal as
Contents. Preface to the Second Edition
Preface to the Second Edition v 1 Introduction 1 1.1 What Is Data Mining?....................... 4 1.2 Motivating Challenges....................... 5 1.3 The Origins of Data Mining....................
Slides credited from Dr. David Silver & Hung-Yi Lee
Slides credited from Dr. David Silver & Hung-Yi Lee Review Reinforcement Learning 2 Reinforcement Learning RL is a general purpose framework for decision making RL is for an agent with the capacity to
STATISTICS (STAT) Statistics (STAT) 1
Statistics (STAT) 1 STATISTICS (STAT) STAT 2013 Elementary Statistics (A) Prerequisites: MATH 1483 or MATH 1513, each with a grade of "C" or better; or an acceptable placement score (see placement.okstate.edu).
Spatial Interpolation - Geostatistics 4/3/2018
Spatial Interpolation - Geostatistics 4/3/201 (Z i Z j ) 2 / 2 Spatial Interpolation & Geostatistics Lag Distance between pairs of points Lag Mean Tobler s Law All places are related, but nearby places
Assessing the Quality of the Natural Cubic Spline Approximation
Assessing the Quality of the Natural Cubic Spline Approximation AHMET SEZER ANADOLU UNIVERSITY Department of Statisticss Yunus Emre Kampusu Eskisehir TURKEY ahsst12@yahoo.com Abstract: In large samples,
ADAPTIVE METROPOLIS-HASTINGS SAMPLING, OR MONTE CARLO KERNEL ESTIMATION
ADAPTIVE METROPOLIS-HASTINGS SAMPLING, OR MONTE CARLO KERNEL ESTIMATION CHRISTOPHER A. SIMS Abstract. A new algorithm for sampling from an arbitrary pdf. 1. Introduction Consider the standard problem of
STATISTICS (STAT) 200 Level Courses. 300 Level Courses. Statistics (STAT) 1
Statistics (STAT) 1 STATISTICS (STAT) 200 Level Courses STAT 250: Introductory Statistics I. 3 credits. Elementary introduction to statistics. Topics include descriptive statistics, probability, and estimation
Flexible Lag Definition for Experimental Variogram Calculation
Flexible Lag Definition for Experimental Variogram Calculation Yupeng Li and Miguel Cuba The inference of the experimental variogram in geostatistics commonly relies on the method of moments approach.
Statistical Modeling with Spline Functions Methodology and Theory
This is page 1 Printer: Opaque this Statistical Modeling with Spline Functions Methodology and Theory Mark H. Hansen University of California at Los Angeles Jianhua Z. Huang University of Pennsylvania
Model combination. Resampling techniques p.1/34
Model combination The winner-takes-all approach is intuitively the approach which should work the best. However recent results in machine learning show that the performance of the final model can be improved
Progressive photon mapping can be interpreted as an estimator that constructs the full paths from camera to light by constantly connecting two fresh
1 Progressive photon mapping can be interpreted as an estimator that constructs the full paths from camera to light by constantly connecting two fresh subpaths. The estimation is a sum, where W_n is the
Petrel TIPS&TRICKS from SCM
Petrel TIPS&TRICKS from SCM Knowledge Worth Sharing Petrel 2D Grid Algorithms and the Data Types they are Commonly Used With Specific algorithms in Petrel s Make/Edit Surfaces process are more appropriate
Introduction to Objective Analysis
Chapter 4 Introduction to Objective Analysis Atmospheric data are routinely collected around the world but observation sites are located rather randomly from a spatial perspective. On the other hand, most
Nonparametric Error Estimation Methods for Evaluating and Validating Artificial Neural Network Prediction Models
Nonparametric Error Estimation Methods for Evaluating and Validating Artificial Neural Network Prediction Models Janet M. Twomey and Alice E. Smith Department of Industrial Engineering University of Pittsburgh
Further Simulation Results on Resampling Confidence Intervals for Empirical Variograms
University of Wollongong Research Online Centre for Statistical & Survey Methodology Working Paper Series Faculty of Engineering and Information Sciences 2010 Further Simulation Results on Resampling Confidence
MODERN FACTOR ANALYSIS
MODERN FACTOR ANALYSIS Harry H. Harman «ö THE pigj UNIVERSITY OF CHICAGO PRESS Contents LIST OF ILLUSTRATIONS GUIDE TO NOTATION xv xvi Parti Foundations of Factor Analysis 1. INTRODUCTION 3 1.1. Brief
An introduction to multi-armed bandits
An introduction to multi-armed bandits Henry WJ Reeve (Manchester) (henry.reeve@manchester.ac.uk) A joint work with Joe Mellor (Edinburgh) & Professor Gavin Brown (Manchester) Plan 1. An introduction to
Direct Sequential Co-simulation with Joint Probability Distributions
Math Geosci (2010) 42: 269 292 DOI 10.1007/s11004-010-9265-x Direct Sequential Co-simulation with Joint Probability Distributions Ana Horta Amílcar Soares Received: 13 May 2009 / Accepted: 3 January 2010
Unified Methods for Censored Longitudinal Data and Causality
Mark J. van der Laan James M. Robins Unified Methods for Censored Longitudinal Data and Causality Springer Preface v Notation 1 1 Introduction 8 1.1 Motivation, Bibliographic History, and an Overview of
Missing Data Analysis for the Employee Dataset
Missing Data Analysis for the Employee Dataset 67% of the observations have missing values! Modeling Setup Random Variables: Y i =(Y i1,...,y ip ) 0 =(Y i,obs, Y i,miss ) 0 R i =(R i1,...,r ip ) 0 ( 1
Use of Extreme Value Statistics in Modeling Biometric Systems
Use of Extreme Value Statistics in Modeling Biometric Systems Similarity Scores Two types of matching: Genuine sample Imposter sample Matching scores Enrolled sample 0.95 0.32 Probability Density Decision
FMA901F: Machine Learning Lecture 3: Linear Models for Regression. Cristian Sminchisescu
FMA901F: Machine Learning Lecture 3: Linear Models for Regression Cristian Sminchisescu Machine Learning: Frequentist vs. Bayesian In the frequentist setting, we seek a fixed parameter (vector), with value(s)
The Immersed Interface Method
The Immersed Interface Method Numerical Solutions of PDEs Involving Interfaces and Irregular Domains Zhiiin Li Kazufumi Ito North Carolina State University Raleigh, North Carolina Society for Industrial
Performance Estimation and Regularization. Kasthuri Kannan, PhD. Machine Learning, Spring 2018
Performance Estimation and Regularization Kasthuri Kannan, PhD. Machine Learning, Spring 2018 Bias- Variance Tradeoff Fundamental to machine learning approaches Bias- Variance Tradeoff Error due to Bias:
Pattern Fabric Defect Detection Using Nonparametric Regression
, Int. J. Appl. Math. Stat.; Vol. 53; Issue No. 6; Year 2015, ISSN 0973-1377 (Print), ISSN 0973-7545 (Online) Copyright 2015 by CESER PUBLICATIONS Pattern Fabric Defect Detection Using Nonparametric Regression
Chapter 1. Introduction
Chapter 1 Introduction A Monte Carlo method is a compuational method that uses random numbers to compute (estimate) some quantity of interest. Very often the quantity we want to compute is the mean of
Hyperparameters and Validation Sets. Sargur N. Srihari
Hyperparameters and Validation Sets Sargur N. srihari@cedar.buffalo.edu 1 Topics in Machine Learning Basics 1. Learning Algorithms 2. Capacity, Overfitting and Underfitting 3. Hyperparameters and Validation
Principles of Robot Motion
Principles of Robot Motion Theory, Algorithms, and Implementation Howie Choset, Kevin Lynch, Seth Hutchinson, George Kantor, Wolfram Burgard, Lydia Kavraki, and Sebastian Thrun A Bradford Book The MIT
ELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Review of Motion Modelling and Estimation Introduction to Motion Modelling & Estimation Forward Motion Backward Motion Block Motion Estimation Motion
Robert Collins CSE598G. Robert Collins CSE598G
Recall: Kernel Density Estimation Given a set of data samples x i ; i=1...n Convolve with a kernel function H to generate a smooth function f(x) Equivalent to superposition of multiple kernels centered
Structural Mechanics: Graph and Matrix Methods
Structural Mechanics: Graph and Matrix Methods A. Kaveh Department of Civil Engineering Technical University of Vienna Austria RESEARCH STUDIES PRESS LTD. Taunton, Somerset, England 0 JOHN WILEY & SONS
Heteroskedasticity and Homoskedasticity, and Homoskedasticity-Only Standard Errors
Heteroskedasticity and Homoskedasticity, and Homoskedasticity-Only Standard Errors (Section 5.4) What? Consequences of homoskedasticity Implication for computing standard errors What do these two terms
Nonparametric Approaches to Regression
Nonparametric Approaches to Regression In traditional nonparametric regression, we assume very little about the functional form of the mean response function. In particular, we assume the model where m(xi)
Topics in Machine Learning-EE 5359 Model Assessment and Selection
Topics in Machine Learning-EE 5359 Model Assessment and Selection Ioannis D. Schizas Electrical Engineering Department University of Texas at Arlington 1 Training and Generalization Training stage: Utilizing
Kernel Density Estimation
Kernel Density Estimation An Introduction Justus H. Piater, Université de Liège Overview 1. Densities and their Estimation 2. Basic Estimators for Univariate KDE 3. Remarks 4. Methods for Particular Domains
Test Oracles and Randomness
Test Oracles and Randomness UNIVERSITÄ T ULM DOCENDO CURANDO SCIENDO Ralph Guderlei and Johannes Mayer rjg@mathematik.uni-ulm.de, jmayer@mathematik.uni-ulm.de University of Ulm, Department of Stochastics
TIME SERIES ANALYSIS FOR IRREGULARLY SAMPLED DATA. Piet M.T. Broersen. Department of Multi Scale Physics, Delft University of Technology
TIME SERIES ANALYSIS FOR IRREGULARLY SAMPLED DATA Piet M.T. Broersen Department of Multi Scale Physics, Delft University of Technology Abstract: Many spectral estimation methods for irregularly sampled
Statistics 406 Exam November 17, 2005
Statistics 406 Exam November 17, 2005 1. For each of the following, what do you expect the value of A to be after executing the program? Briefly state your reasoning for each part. (a) X
Aaron Daniel Chia Huang Licai Huang Medhavi Sikaria Signal Processing: Forecasting and Modeling
Aaron Daniel Chia Huang Licai Huang Medhavi Sikaria Signal Processing: Forecasting and Modeling Abstract Forecasting future events and statistics is problematic because the data set is a stochastic, rather
From Par(cle Filters to Malliavin Filtering with Applica(on to Target Tracking Sérgio Pequito Phd Student
From Par(cle Filters to Malliavin Filtering with Applica(on to Target Tracking Sérgio Pequito Phd Student Final project of Detection, Estimation and Filtering course July 7th, 2010 Guidelines Mo(va(on
RESAMPLING METHODS. Chapter 05
1 RESAMPLING METHODS Chapter 05 2 Outline Cross Validation The Validation Set Approach Leave-One-Out Cross Validation K-fold Cross Validation Bias-Variance Trade-off for k-fold Cross Validation Cross Validation
1 Methods for Posterior Simulation
1 Methods for Posterior Simulation Let p(θ y) be the posterior. simulation. Koop presents four methods for (posterior) 1. Monte Carlo integration: draw from p(θ y). 2. Gibbs sampler: sequentially drawing
Preface to the Second Edition. Preface to the First Edition. 1 Introduction 1
Preface to the Second Edition Preface to the First Edition vii xi 1 Introduction 1 2 Overview of Supervised Learning 9 2.1 Introduction... 9 2.2 Variable Types and Terminology... 9 2.3 Two Simple Approaches
Lecture 27: Review. Reading: All chapters in ISLR. STATS 202: Data mining and analysis. December 6, 2017
Lecture 27: Review Reading: All chapters in ISLR. STATS 202: Data mining and analysis December 6, 2017 1 / 16 Final exam: Announcements Tuesday, December 12, 8:30-11:30 am, in the following rooms: Last
Three challenges in route choice modeling
Three challenges in route choice modeling Michel Bierlaire and Emma Frejinger transp-or.epfl.ch Transport and Mobility Laboratory, EPFL Three challenges in route choice modeling p.1/61 Route choice modeling
LAB #2: SAMPLING, SAMPLING DISTRIBUTIONS, AND THE CLT
NAVAL POSTGRADUATE SCHOOL LAB #2: SAMPLING, SAMPLING DISTRIBUTIONS, AND THE CLT Statistics (OA3102) Lab #2: Sampling, Sampling Distributions, and the Central Limit Theorem Goal: Use R to demonstrate sampling
Minima, Maxima, Saddle points
Minima, Maxima, Saddle points Levent Kandiller Industrial Engineering Department Çankaya University, Turkey Minima, Maxima, Saddle points p./9 Scalar Functions Let us remember the properties for maxima,
The Bootstrap. Philip M. Dixon Iowa State University,
Statistics Preprints Statistics 12-2001 The Bootstrap Philip M. Dixon Iowa State University, pdixon@iastate.edu Follow this and additional works at: http://lib.dr.iastate.edu/stat_las_preprints Part of
Passive Differential Matched-field Depth Estimation of Moving Acoustic Sources
Lincoln Laboratory ASAP-2001 Workshop Passive Differential Matched-field Depth Estimation of Moving Acoustic Sources Shawn Kraut and Jeffrey Krolik Duke University Department of Electrical and Computer
Curves and Surfaces for Computer-Aided Geometric Design
Curves and Surfaces for Computer-Aided Geometric Design A Practical Guide Fourth Edition Gerald Farin Department of Computer Science Arizona State University Tempe, Arizona /ACADEMIC PRESS I San Diego
Analysis of Incomplete Multivariate Data
Analysis of Incomplete Multivariate Data J. L. Schafer Department of Statistics The Pennsylvania State University USA CHAPMAN & HALL/CRC A CR.C Press Company Boca Raton London New York Washington, D.C.
4.5 The smoothed bootstrap
4.5. THE SMOOTHED BOOTSTRAP 47 F X i X Figure 4.1: Smoothing the empirical distribution function. 4.5 The smoothed bootstrap In the simple nonparametric bootstrap we have assumed that the empirical distribution
Package npcp. August 23, 2017
Type Package Package np August 23, 2017 Title Some Nonparametric CUSUM Tests for Change-Point Detection in Possibly Multivariate Observations Version 0.1-9 Date 2017-08-23 Author Ivan Kojadinovic Maintainer
Package iosmooth. R topics documented: February 20, 2015
Package iosmooth February 20, 2015 Type Package Title Functions for smoothing with infinite order flat-top kernels Version 0.91 Date 2014-08-19 Author, Dimitris N. Politis Maintainer
Estimating and Sampling Graphs with Multidimensional Random Walks. Group 2: Mingyan Zhao, Chengle Zhang, Biao Yin, Yuchen Liu
Estimating and Sampling Graphs with Multidimensional Random Walks Group 2: Mingyan Zhao, Chengle Zhang, Biao Yin, Yuchen Liu Complex Network Motivation Social Network Biological Network erence: www.forbe.com
Generalized Additive Models
:p Texts in Statistical Science Generalized Additive Models An Introduction with R Simon N. Wood Contents Preface XV 1 Linear Models 1 1.1 A simple linear model 2 Simple least squares estimation 3 1.1.1
Nonparametric regression using kernel and spline methods
Nonparametric regression using kernel and spline methods Jean D. Opsomer F. Jay Breidt March 3, 016 1 The statistical model When applying nonparametric regression methods, the researcher is interested
Bootstrapping Methods
Bootstrapping Methods example of a Monte Carlo method these are one Monte Carlo statistical method some Bayesian statistical methods are Monte Carlo we can also simulate models using Monte Carlo methods
The Cross-Entropy Method
The Cross-Entropy Method Guy Weichenberg 7 September 2003 Introduction This report is a summary of the theory underlying the Cross-Entropy (CE) method, as discussed in the tutorial by de Boer, Kroese,
Cross-validation. Cross-validation is a resampling method.
Cross-validation Cross-validation is a resampling method. It refits a model of interest to samples formed from the training set, in order to obtain additional information about the fitted model. For example,
Statistical Matching using Fractional Imputation
Statistical Matching using Fractional Imputation Jae-Kwang Kim 1 Iowa State University 1 Joint work with Emily Berg and Taesung Park 1 Introduction 2 Classical Approaches 3 Proposed method 4 Application:
Lab Session 1. Introduction to Eviews
Albert-Ludwigs University Freiburg Department of Empirical Economics Time Series Analysis, Summer 2009 Dr. Sevtap Kestel To see the data of m1: 1 Lab Session 1 Introduction to Eviews We introduce the basic
Nonparametric Importance Sampling for Big Data
Nonparametric Importance Sampling for Big Data Abigael C. Nachtsheim Research Training Group Spring 2018 Advisor: Dr. Stufken SCHOOL OF MATHEMATICAL AND STATISTICAL SCIENCES Motivation Goal: build a model