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正版现货新书基于回归观点的统计学习第3版 9787523211328 (美)理查德·伯克|

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作者(美)理查德·伯克|

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ISBN9787523211328

出版时间2024-03

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定价179元

货号31997802

上书时间2025-01-04

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商品描述: 目录
1  Statistical Learning as a Regression Problem
  1.1  Getting Started
  1.2  Setting the Regression Context
  1.3  Revisiting the Ubiquitous Linear Regression Model
    1.3.1  Problems in Practice
  1.4  Working with Statistical Models that are Wrong
    1.4.1  An Alternative Approach to Regression
    1.4.2  More on Statistical Inference with Wrong Models
    1.4.3  Introduction to Sandwich Standard Errors
    1.4.4  Introduction to Conformal Inference
    1.4.5  Introduction to the Nonparametric Bootstrap
    1.4.6  Wrong Regression Models with Binary Response Variables
  1.5  The Transition to Statistical Learning
    1.5.1  Models Versus Algorithms
  1.6  Some Initial Concepts
    1.6.1  Overall Goals of Statistical Learning
    1.6.2  Forecasting with Supervised Statistical Learning
    1.6.3  Overfitting
    1.6.4  Data Snooping
    1.6.5  Some Constructive Responses to Overfitting and Data Snooping
    1.6.6  Loss Functions and Related Concepts
    1.6.7  The Bias-Variance Tradeoff
    1.6.8  Linear Estimators
    1.6.9  Degrees of Freedom
    1.6.10  Basis Functions
    1.6.11  The Curse of Dimensionality
  1.7  Statistical Learning in Context
  Endnotes
  References
2  Splines, Smoothers, and Kernels
  2.1  Introduction
  2.2  Regression Splines
    2.2.1  Piecewise Linear Population Approximations
    2.2.2  Polynomial Regression Splines
    2.2.3  Natural Cubic Splines
    2.2.4  B-Splines
  2.3  Penalized Smoothing
    2.3.1  Shrinkage and Regularization
  2.4  Penalized Regression Splines
    2.4.1  An Application
  2.5  Smoothing Splines
    2.5.1  A Smoothing Splines Illustration
  2.6  Locally Weighted Regression as a Smoother
    2.6.1  Nearest Neighbor Methods
    2.6.2  Locally Weighted Regression
  2.7  Smoothers for Multiple Predictors
    2.7.1  Smoothing in Two Dimensions
    2.7.2  The Generalized Additive Model
  2.8  Smoothers with Categorical Variables
    2.8.1  An Illustration Using the Generalized Additive Model with a Binary Outcome
  2.9  An Illustration of Statistical Inference After Model Selection
    2.9.1  Level I Versus Level II Summary
  2.10  Kernelized Regression
    2.10.1  Radial Basis Kernel
    2.10.2  ANOVA Radial Basis Kernel
    2.10.3  A Kernel Regression Application
  2.11  Summary and Conclusions
  Endnotes
  References
3  Classification and Regression Trees (CART)
  3.1  Introduction
  3.2  An Introduction to Recursive Partitioning in CART
  3.3  The Basic Ideas in More Depth
    3.3.1  Tree Diagrams for Showing What the Greedy Algorithm Determined
    3.3.2  An Initial Application
    3.3.3  Classification and Forecasting with CART
    3.3.4  Confusion Tables
    3.3.5  CART as an Adaptive Nearest Neighbor Method
  3.4  The Formalities of Splitting a Node
  3.5  An Illustrative Prison Inmate Risk Assessment Using CART ...
  3.6  Classification Errors and Costs
    3.6.1  Default Costs in CART
    3.6.2  Prior Probabilities and Relative Misclassification Costs
  3.7  Varying the Prior and the Complexity Parameter
  3.8  An Example with Three Response Categories
  3.9  Regression Trees
    3.9.1  A CART Application for the Correlates of a Student's GPA in High School
  3.10  Pruning
  3.11  Missing Data
    3.11.1  Missing Data with CART
  3.12  More on CART Instability
  3.13  Summary of Statistical Inference with CART
    3.13.1  Summary of Statistical Inference for CART Forecasts
  3.14  Overall Summary and Conclusions
  Exercises
  Endnotes
  References
4  Bagging
  4.1  Introduction
  4.2  The Bagging Algorithm
  4.3  Some Bagging Details
    4.3.1  Revisiting the CART Instability Problem
    4.3.2  Resampling Methods for Bagging
    4.3.3  Votes Over Trees and Probabilities
    4.3.4  Forecasting and Imputation
    4.3.5  Bagging Estimation and Statistical Inference
    4.3.6  Margins for Classification
    4.3.7  Using Out-of-Bag Observations as Test Data
    4.3.8  Baggi

内容摘要
本书把关注点集中在给定一组预测变量并且在数据分析开始之前缺乏可以指定的可靠模型时的响应变量的条件分布上。与现代数据分析一致，它强调适当的统计学习数据分析以综合方式依赖于健全的数据收集、智能数据管理、适当的统计程序和对结果的可理解的解释。
监督学习可被统一视为回归分析的一种形式。通过大量实际应用及其相关的R代码
来说明关键概念和过程，着
眼于实际意义。计算机科学和统计学的日益融合在这本教材中得到了很好的体现。