Virtual Vintage: The Insider's Guide to Buying and Selling Fashion Online
Author: Linda Lindroth
Vintage clothing has never been more chic, with everyone from celebrity trendsetters to style-conscious professionals searching for wearable treasures from the past. Virtual Vintage is the first and only guide that helps both the novice and the fashion connoisseur evaluate and confidently participate in the thriving vintage marketplace that exists online. No other book explains how to get it, sell it, fix it, or wear it with flair. Authors Linda Lindroth and Deborah Newell Tornello equip readers from head to toe with
• more than 100 chic sites—rated and evaluated
• instructions on contacting sellers
• smart strategies for bidding in online auctions
• advice about evaluating the size, quality, and colors of a garment
• tips for cleaning and repairing vintage items
Whether you’re looking for a 1960s Rudi Gernreich knit, Gucci hipster trousers, a Claire McCardell for Townley shirtwaist, or a Chanel suit in pink wool with black patent-leather trim, Virtual Vintage will help you build a unique and sensational wardrobe.
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Statistical Computing with R
Author: Maria L Rizzo
Focusing on implementation rather than theory, Statistical Computing with R serves as a valuable tutorial, providing examples that illustrate programming concepts in the context of practical computational problems. This book presents an overview of computational statistics with an introduction to the R computing environment. Reviewing basic concepts in probability and classical statistical inference, the text demonstrates every algorithm through fully implemented examples coded in R. Chapters cover topics such as Monte Carlo methods, clustering, bootstrap, nonparametric regression, density estimation, and goodness-of-fit. Many exercises are included for the students while a solutions manual is included for the instructor.
Table of Contents:
Preface xv
1 Introduction 1
1.1 Computational Statistics and Statistical Computing 1
1.2 The R Environment 3
1.3 Getting Started with R 4
1.4 Using the R Online Help System 7
1.5 Functions 8
1.6 Arrays, Data Frames, and Lists 9
1.7 Workspace and Files 15
1.8 Using Scripts 17
1.9 Using Packages 18
1.10 Graphics 19
2 Probability and Statistics Review 21
2.1 Random Variables and Probability 21
2.2 Some Discrete Distributions 25
2.3 Some Continuous Distributions 29
2.4 Multivariate Normal Distribution 33
2.5 Limit Theorems 35
2.6 Statistics 35
2.7 Bayes' Theorem and Bayesian Statistics 40
2.8 Markov Chains 42
3 Methods for Generating Random Variables 47
3.1 Introduction 47
3.2 The Inverse Transform Method 49
3.3 The Acceptance-Rejection Method 55
3.4 Transformation Methods 58
3.5 Sums and Mixtures 61
3.6 Multivariate Distributions 69
3.7 Stochastic Processes 82
Exercises 94
4 Visualization of Multivariate Data 97
4.1 Introduction 97
4.2 Panel Displays 97
4.3 Surface Plots and 3D Scatter Plots 100
4.4 Contour Plots 106
4.5 Other 2D Representations of Data 110
4.6 Other Approaches to Data Visualization 115
Exercises 116
5 Monte Carlo Integration and Variance Reduction 119
5.1 Introduction 119
5.2 Monte Carlo Integration 119
5.3 Variance Reduction 126
5.4 Antithetic Variables 128
5.5 Control Variates 132
5.6 Importance Sampling 139
5.7 Stratified Sampling 144
5.8 Stratified Importance Sampling 147
Exercises 149
R Code 152
6 Monte Carlo Methods in Inference 153
6.1 Introduction 153
6.2 Monte Carlo Methods for Estimation 154
6.3 Monte Carlo Methods for Hypothesis Tests 162
6.4 Application174
Exercises 180
7 Bootstrap and Jackknife 183
7.1 The Bootstrap 183
7.2 The Jackknife 190
7.3 Jackknife-after-Bootstrap 195
7.4 Bootstrap Confidence Intervals 197
7.5 Better Bootstrap Confidence Intervals 203
7.6 Application 207
Exercises 212
8 Permutation Tests 215
8.1 Introduction 215
8.2 Tests for Equal Distributions 219
8.3 Multivariate Tests for Equal Distributions 222
8.4 Application 235
Exercises 242
9 Markov Chain Monte Carlo Methods 245
9.1 Introduction 245
9.2 The Metropolis-Hastings Algorithm 247
9.3 The Gibbs Sampler 263
9.4 Monitoring Convergence 266
9.5 Application 271
Exercises 277
R Code 279
10 Probability Density Estimation 281
10.1 Univariate Density Estimation 281
10.2 Kernel Density Estimation 296
10.3 Bivariate and Multivariate Density Estimation 305
10.4 Other Methods of Density Estimation 314
Exercises 314
R Code 317
11 Numerical Methods in R 319
11.1 Introduction 319
11.2 Root-finding in One Dimension 326
11.3 Numerical Integration 330
11.4 Maximum Likelihood Problems 335
11.5 One-dimensional Optimization 338
11.6 Two-dimensional Optimization 342
11.7 The EM Algorithm 345
11.8 Linear Programming - The Simplex Method 348
11.9 Application 349
Exercises 353
A Notation 355
B Working with Data Frames and Arrays 357
B.1 Resampling and Data Partitioning 357
B.2 Subsetting and Reshaping Data 360
B.3 Data Entry and Data Analysis 364
References 375
Index 395
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