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Bowman A.W., Azzalini A. — Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations
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Название: Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations
Авторы: Bowman A.W., Azzalini A.
Аннотация: This book describes the use of smoothing techniques in statistics and includes both density estimation and nonparametric regression. Incorporating recent advances, it describes a variety of ways to apply these methods to practical problems. Although the emphasis is on using smoothing techniques to explore data graphically, the discussion also covers data analysis with nonparametric curves, as an extension of more standard parametric models. Intended as an introduction, with a focus on applications rather than on detailed theory, the book will be equally valuable for undergraduate and graduate students in statistics and for a wide range of scientists interested in statistical techniques.
The text makes extensive reference to S-Plus, a powerful computing environment for exploring data, and provides many S-Plus functions and example scripts. This material, however, is independent of the main body of text and may be skipped by readers not interested in S-Plus.
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Рубрика: Computer science /
Статус предметного указателя: Готов указатель с номерами страниц
ed2k: ed2k stats
Год издания: 1997
Количество страниц: 193
Добавлена в каталог: 18.12.2008
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Предметный указатель
Nychka, D. 84 182
Observed significance 96 (see also -value)
Orthogonal series, method of see Projection method
Ostrowsky, E. 180
Owari - Wadunde, A. 185
Park, B. U. 10 34 180-182
Parzen, E. 3 129 182
Pearson, G. W. 48 182
percentile 60-62 174
Permutation test 44 80 86 87 89 110 112
Pitcher, C. R. 183
Plot, contour 7 9-12 23 24 32 35 43 44 127
Plot, image 7-9
Plot, probability 38 39
Plot, probability, Mahalanobis 39
Plot, scatter 6-8 10 36 42 48 54 57 58 60 61 91 92
Plot, slice 7-10
Poiner, I. R. 52 183
Poisson distribution 57 100 104 167 173
Prader, A. 179
Prediction interval 101
Pregibon, D. 182
Priede, I. G. 154 176 183 185
Priestley, I. B. 129 183
Profile 137 138 140 142 148 173
Proh, D. 183
Projection method 19 20 65 66
Projection pursuit 40 167
Proportional hazards model 62
Prvan, T. 35 176
Pseudo-likelihood ratio test 87-104 172 173
Purcell, E. 176
Qua, F. 48 182
Quadratic form 87 88 92 93 98 118 119 121 125
Quantile 59
Quesenberry, C. P. 18 182
Raid, T. 183
Rank test 110
Ratkowsky, D. A. 123 183
Raz, J. 80 140 183
Reference bands 41 42 47 81 82 89-91 93 94 110 119 120 122 172 173
Regression function 48 65 75 78 84 98 103 117 118 136 151 167 173
Relative risk 112-115
Repeated measurements 137 139 140 144 148 173 longitudinal)
Residual sum of squares 74 75 80 84 86 87 119 120 139 144 153 157
Residuals 65 82 91 92 94 96 139 144- 146 151 152
Rice, J. A. 74 139 147 183
Richardson, S. J. 62 183
Ripley, B. D. 23 183
Robinson, P. M. 133 134 147 183
Robustness 64 105
Rosenberg, P. S. 22 183
Rosenblatt, M. 3 38 129 133 147 176 179 183
Roughness penalty 65
Roussas, G. G. 147 175 183
Rowlingson, B. 126
Rowlingson, B. S. 178
Royston, P. 167 183
Rudemo, M. 32 184
Ruppert, D. 53 70 72 78 184 185
Ryan, B. F. 96 184
Ryan, T. A. Jr 184
Sacks, J. 182
Salini, J. P. 183
Sarda, P. 180
Saviotti, P. P. 184
Schou, G. 175
Schwartz, S. C. 20 184
Scott, D. W. 10-12 21 29 32 34 45 46 184 185
Scott, E. M. 184
Seheult, A. 180
Seifert, B. 75 184
Semiparametric approach 148
Semiparametric density estimation 20
Semiparametric regression 167
Senikas, V. 183
Severini, T. A. 104 184
Shapiro, S. S. 38 39 184
Sheather - Jones method see Bandwidth selection plug-in
Sheather, S. J. 34 35 42 46 47 181 184
Shi, M. 148 184
Shoemaker, A. C. 182
Shyu, W. M. 177
Significance trace 89 93-96 105 106 125- 127 171
Silverman, B. W. 14 17 21 22 35 45 59 65 84 147 150 163 164 167 180 183 184
Simonoff, J. S. 22 30 184
skewness 1 2 14 33 38 41 59 60 83 135
Sliced inverse regression 167
Smoothing matrix 72 87 124 138 153 156
Smoothing parameter see Bandwidth
Spatial variation 112 116 164
Speckman, P. L. 75 124 126 178 184
Spectral density 129
Sphere 12 173 174
Spiegelman, N. H. 104 178
Splines, smoothing 64 148 153
Splines, smoothing, multivariate adaptive regression 167
Sroka, L. 179
Standard deviation 3 21 31 32 37 40 49 69-71 84 110 111 114 115 120 170 171 173
Staniswalis, J. G. 104 184
StatLib 148
Stephens, M. A. 38 184
Stone, C. J. 32 185
Stone, M. A. 32 185
Stuetzle, W. 150 167 177 179
Sugden, D. E. 178
Survival data see Data survival
Tapia, R. A. 184
Tarter, I. E. 20 182
Taylor series expansion 25-29 47 73 127
Taylor, J. M. G. 184
Taylor, N. C. 45 185
Terrell, G. R. 32 34 184 185
Test statistic 38 39 43 46 47 80 88 94 98 102 103 105 109 116 118 119 126 127
Thomas, M. R. 183
Thomas, R. W. 178
Thompson, J. R. 184
Tibshirani, R. 62 67 71 75 78 150 151 153 154 161-164 166- 168 177 181 185
Titterington, D. M. 84 114 116 126 175 180
Toscas, P. 183
Transformation 172
Transformation, logarithmic 96 135
Transformation, logistic 24 27
Transformation, smooth 167
Transformation, square root 29 114 116 127
Transformation, variance stabilising 109 114 167
Tsybakov, A. B. 135 147 181
Tukey, J. W. 29 185
Van Houwelingen, J. C. 104 182
Variability bands 29 30 45 46 75 76 80 81 83 85 89 105 140 153 172 173
Variance, estimation of 73-75 84 90 104 173
Varying coefficient model 160-162
Vasicek, D. 46 185
Veronise, S. 183
Vieu, P. 135 180 181
von Mises distribution 13
Wahba, G. 65 84 177 185
Wand, M. P. 15 16 21 22 26 33 46 53 67 70 72 179 182 184 185
Wang, Y. G. 183
Wassenberg, T. J. 183
Watson, G. S. 22 49 185
Watson, J. J. 154 183 185
Wavelets 65 66
Wehrly, O. E. 139 140 181 182
Weidong, P. 68 120 185
Weisberg, S. 10 104 177 185
Weiss, R. E. 184
Weixing, W. 185
Welsh, A. H. 104 185
Whitehead, R. R. 185
Whittle, P. 3 185
Wilk, I. B. 38 39 184
Witthames, P. R. 185
Wolf, A. 184
World Wide Web 148 169
Wright, E. M. 61 185
Xianmin, Z. 185
Xiaomin, C. 185
Yackel, J. W. 18 182
Yandell, B. S. 61 177 178
Yang, Y. 185
Ylvisaker, D. 182
Yongxing, Y. 185
Young, S. G. 12 121 126 185 186
Zeger, S. L. 148 186
Zhao, H. 178
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