<h2 class="TxtB"><font size="3">高清扫描版，约13M左右</font></h2><h2 class="TxtB"> </h2><h2 class="TxtB"><font size="3">Smoothing Methods in Statistics</font></h2><p class="TxtB"><strong>Series:</strong> springer series in statistics</p><p class="TxtB"><strong>Author:</strong> Simonoff, Jeffrey S</p><p class="TxtB"><strong>About This Book:</strong></p><p class="TxtB">Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the "Background Material" sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the "Computational Issues" sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing.</p><p class="TxtB"><strong>Writtern For:</strong></p><p class="TxtB">Graduate Students, Researchers </p><p class="TxtB"><strong></strong> </p><p class="TxtB"><strong>Contents</strong></p><strong></strong><p class="TxtB"><br/><br/>1. Introduction<br/></p><p class="TxtB">1.1 Smoothing Methods: a Nonparametric-Parametric Compromise<br/>1.2 U ses of Smoothing Methods<br/>1.3 Outline of the Chapters<br/>Background material<br/>Computational issues<br/>Exercises<br/><br/>2. Simple Univariate Density Estimation<br/>2.1 The Histogram<br/>2.2 The Frequency Polygon<br/>2.3 Varying the Bin Width<br/>2.4 The Effectiveness of Simple Density Estimators<br/>Background material<br/>Computational issues<br/>Exercises<br/></p><p class="TxtB">3. Smoother Univariate Density Estimation 40<br/>3.1 Kernel Density Estimation 40<br/>3 2 Problems with Kernel Density Estimation 49<br/>3.3 Adjustments and Improvements to Kernel Density Estimation 53<br/>3.4 Local Likelihood Estimation 64<br/>3.5 Roughness Penalty and Spline-Based Methods 67<br/>3.6 Comparison of Univariate Density Estimators 70<br/>Background material 72<br/>Computational issue 92<br/>Exercises 94<br/></p><p class="TxtB">4. Multivariate Density Estimation<br/>4.1 Simple Density Estimation Methods<br/>4.2 Kernel Density Estimation<br/>4.3 Other Estimators 111<br/>4.4 Dimension Rβduction and Projection Pursuit 117<br/>4 5 The State of Multivariate Density Estimation 121<br/>Background material 123<br/>Computational issues 131<br/>Exercises 132<br/></p><p class="TxtB">5. Nonparametric Regression 134<br/>5.1 Scatter Plot Smoothing and Kernel Regression 134<br/>5.2 Local Polynomial Regression 138<br/>5.3 Bandwidth Selection 151<br/>54 Locally Varying the Bandwidth 154<br/>5 5 Outliers and Autocorrelation 160<br/>5.6 Spline Smoothing 168<br/>5.7 Multiple Predictors and Additive Models 178<br/>58 Comparing Nonparametric Regression Methods 190<br/>Background material 191<br/>Computational issues 210<br/>Exercises 212<br/></p><p class="TxtB">6. Smoothing Ordered Categorica1 Data 215<br/>6.1 Smoothing and Ordered Categorical Data 215<br/>6.2 Smoothing Sparse Multinomials 217<br/>6.3 Smoothing Sparse Contingency Tables 226<br/>6.4 Categorical Data, Regression, and Density Estimation 236<br/>Background material 243<br/>Computational issues 250<br/>Exercises 250<br/></p><p class="TxtB">7. Further Applications of Smoothing 252<br/>7.1 Discriminant Analysis 252<br/>7.2 Goodness-of-Fit Tests 258<br/>7.3 Smoothing-Based Parametric Estimation 261<br/>7.4 The Smoothed Bootstrap 266<br/>Background material 268<br/>Computational issues 273<br/>Exercises 273<br/></p><p class="TxtB">Appendices 275<br/>A. Descriptions of the Data Sets 275<br/>B. More on Computational Issues 288<br/>References 290<br/>Author Index 321<br/>Subject Index 329</p><h2 class="TxtB"> </h2>