Statistical Learning Introduction with R Applications is an indispensable resource for anyone looking to explore the world of statistical learning with the powerful R software. This book covers essential topics in statistical learning, encompassing both supervised and unsupervised learning techniques. Readers will gain insights into data mining, inference, and the practical applications of statistical learning using R. Whether you're a data analyst, researcher, or student, this resource equips you with the knowledge and skills necessary to harness the capabilities of R for statistical learning tasks. With a focus on practicality, it offers a hands-on approach to mastering statistical learning concepts and their real-world applications.
Author(s): Educohack Press
Publisher: Educohack Press
Year: 2023
Language: English
Pages: 1252
Expectations...........................................2
Prerequisites and Further Reading............................3
Styles and Fonts........................................4
An Introduction to R.....................................5
Getting Started.....................................6
Probability Distributions in R...........................15
Graphical Facilities.................................16
Writing New R Functions.............................19
Input and Output in R................................21
Administration of R Objects...........................21
Outlier Detection.......................................58
Exercises............................................61
Linear Models.........................................66
Classical Least Squares Estimator............................69
The Jeffreys Prior Analysis.................................73
Zellner’s G-Prior Analysis.................................74
A Semi-noninformative Solution.........................75
Bayes Factors and Model Comparison.....................81
Prediction........................................84
Markov Chain Monte Carlo Methods.........................85
Conditionals......................................86
Two-Stage Gibbs Sampler.............................87
The General Gibbs Sampler............................90
Variable Selection......................................91
Deciding on Explanatory Variables.......................91
G-Prior Distributions for Model Choice....................93
A Stochastic Search for the Most Likely Model...............96
Exercises............................................98
A Generalization of the Linear Model........................104
Motivation......................................104
Link Functions....................................106
Metropolis–Hastings Algorithms...........................108
The Independence Sampler............................110
The Random Walk Sampler...........................111
Output Analysis and Proposal Design.....................111
The Probit Model......................................115
Flat Prior.......................................115
Noninformative G-Priors.............................117
About Informative Prior Analyses.......................122
The Logit Model......................................124
Log-Linear Models....................................127
Contingency Tables................................127
Inference Under a Flat Prior...........................131
Exercises...........................................137
Inference in a Finite Population............................140
Sampling Models......................................142
The Binomial Capture Model..........................142
The Two-Stage Capture–Recapture Model.................143
The T -Stage Capture–Recapture Model...................148
Open Populations......................................152
Accept–Reject Algorithms................................156
The Arnason–Schwarz Capture–Recapture Model................160
Modeling.......................................161
Gibbs Sampler....................................165
Exercises...........................................168
Missing Variable Models.................................174
Finite Mixture Models..................................176
Mixture Likelihoods and Posteriors..........................177
MCMC Solutions......................................182
Label Switching Difficulty................................192
Prior Selection........................................198
Tempering..........................................199
Mixtures with an Unknown Number of Components...............201
Exercises...........................................206
Time-Indexed Data....................................210
Setting 210
Stability of Time Series..............................212
Autoregressive (AR) Models..............................214
The Models......................................215
Moving Average (MA) Models.............................226
ARMA Models and Other Extensions........................232
Hidden Markov Models.................................236
Basics 237
Forward–Backward Representation......................241
Exercises...........................................248
Image Analysis as a Statistical Problem.......................252
Spatial Dependence....................................252
Grids and Lattices..................................252
Markov Random Fields..............................254
The Ising Model...................................256
The Potts Model...................................260
Handling the Normalizing Constant..........................262
Path Sampling....................................264
The ABC Method..................................267
Inference on Potts Models............................270
Image Segmentation....................................273
Exercises...........................................281
References.............................................287
Index.................................................291
