应《网络安全法》要求，自2017年10月1日起，未进行实名认证将不得使用互联网跟帖服务。为保障您的帐号能够正常使用，请尽快对帐号进行手机号验证，感谢您的理解与支持！

24小时热门版块排行榜    

小木虫论坛-学术科研互动平台 » 专业学科区 » 数学 » 概率论与统计 » Spatial Statistics and Spatio-Temporal Data
291/1返回列表
查看: 2055  |  回复: 28
只看楼主 @他人 存档 新回复提醒 (忽略) 收藏    在APP中查看
【奖励】 本帖被评价26次，作者pkusiyuan增加金币 20.8 个

pkusiyuan

银虫 (正式写手)

[资源] Spatial Statistics and Spatio-Temporal Data

Contents
Preface xi
1 Introduction 1
1.1 Stationarity 4
1.2 The effect of correlation in estimation and prediction 5
1.2.1 Estimation 5
1.2.2 Prediction 12
1.3 Texas tidal data 14
2 Geostatistics 21
2.1 A model for optimal prediction and error assessment 23
2.2 Optimal prediction (kriging) 25
2.2.1 An example: phosphorus prediction 28
2.2.2 An example in the power family of variogram
functions 32
2.3 Prediction intervals 34
2.3.1 Predictions and prediction intervals for lognormal
observations 35
2.4 Universal kriging 38
2.4.1 Optimal prediction in universal kriging 39
2.5 The intuition behind kriging 40
2.5.1 An example: the kriging weights in the
phosphorus data 41
3 Variogram and covariance models and estimation 45
3.1 Empirical estimation of the variogram or covariance
function 45
3.1.1 Robust estimation 46
3.1.2 Kernel smoothing 47
3.2 On the necessity of parametric variogram and
covariance models 47
vi CONTENTS
3.3 Covariance and variogram models 48
3.3.1 Spectral methods and the Matérn covariance model 51
3.4 Convolution methods and extensions 55
3.4.1 Variogram models where no covariance function
exists 56
3.4.2 Jumps at the origin and the nugget effect 56
3.5 Parameter estimation for variogram and covariance
models 57
3.5.1 Estimation with a nonconstant mean function 62
3.6 Prediction for the phosphorus data 63
3.7 Nonstationary covariance models 69
4 Spatial models and statistical inference 71
4.1 Estimation in the Gaussian case 74
4.1.1 A data example: model fitting for the wheat
yield data 75
4.2 Estimation for binary spatial observations 78
4.2.1 Edge effects 83
4.2.2 Goodness of model fit 84
5 Isotropy 87
5.1 Geometric anisotropy 91
5.2 Other types of anisotropy 92
5.3 Covariance modeling under anisotropy 93
5.4 Detection of anisotropy: the rose plot 94
5.5 Parametric methods to assess isotropy 96
5.6 Nonparametric methods of assessing anisotropy 97
5.6.1 Regularly spaced data case 97
5.6.2 Irregularly spaced data case 101
5.6.3 Choice of spatial lags for assessment of isotropy 104
5.6.4 Test statistics 105
5.6.5 Numerical results 107
5.7 Assessment of isotropy for general sampling designs 111
5.7.1 A stochastic sampling design 111
5.7.2 Covariogram estimation and asymptotic properties 112
5.7.3 Testing for spatial isotropy 113
5.7.4 Numerical results for general spatial designs 115
5.7.5 Effect of bandwidth and block size choice 117
5.8 An assessment of isotropy for the longleaf pine sizes 120
CONTENTS vii
6 Space–time data 123
6.1 Space–time observations 123
6.2 Spatio-temporal stationarity and spatio-temporal
prediction 124
6.3 Empirical estimation of the variogram, covariance
models, and estimation 125
6.3.1 Space–time symmetry and separability 126
6.4 Spatio-temporal covariance models 127
6.4.1 Nonseparable space–time covariance models 128
6.5 Space–time models 130
6.6 Parametric methods of assessing full symmetry and
space–time separability 132
6.7 Nonparametric methods of assessing full symmetry
and space–time separability 133
6.7.1 Irish wind data 139
6.7.2 Pacific Ocean wind data 141
6.7.3 Numerical experiments based on the Irish
wind data 142
6.7.4 Numerical experiments on the test for separability
for data on a grid 144
6.7.5 Taylor’s hypothesis 145
6.8 Nonstationary space–time covariance models 147
7 Spatial point patterns 149
7.1 The Poisson process and spatial randomness 150
7.2 Inhibition models 156
7.3 Clustered models 158
8 Isotropy for spatial point patterns 167
8.1 Some large sample results 169
8.2 A test for isotropy 170
8.3 Practical issues 171
8.4 Numerical results 173
8.4.1 Poisson cluster processes 173
8.4.2 Simple inhibition processes 176
8.5 An application to leukemia data 177
9 Multivariate spatial and spatio-temporal models 181
9.1 Cokriging 183
9.2 An alternative to cokriging 186
viii CONTENTS
9.2.1 Statistical model 187
9.2.2 Model fitting 188
9.2.3 Prediction 191
9.2.4 Validation 192
9.3 Multivariate covariance functions 194
9.3.1 Variogram function or covariance function? 195
9.3.2 Intrinsic correlation, separable models 196
9.3.3 Coregionalization and kernel convolution
models 197
9.4 Testing and assessing intrinsic correlation 198
9.4.1 Testing procedures for intrinsic correlation and
symmetry 201
9.4.2 Determining the order of a linear model of
coregionalization 202
9.4.3 Covariance estimation 204
9.5 Numerical experiments 205
9.5.1 Symmetry 205
9.5.2 Intrinsic correlation 207
9.5.3 Linear model of coregionalization 209
9.6 A data application to pollutants 209
9.7 Discussion 213
10 Resampling for correlated observations 215
10.1 Independent observations 218
10.1.1 U-statistics 218
10.1.2 The jackknife 220
10.1.3 The bootstrap 221
10.2 Other data structures 224
10.3 Model-based bootstrap 225
10.3.1 Regression 225
10.3.2 Time series: autoregressive models 227
10.4 Model-free resampling methods 228
10.4.1 Resampling for stationary dependent
observations 230
10.4.2 Block bootstrap 232
10.4.3 Block jackknife 233
10.4.4 A numerical experiment 233
10.5 Spatial resampling 236
10.5.1 Model-based resampling 237
10.5.2 Monte Carlo maximum likelihood 238
CONTENTS ix
10.6 Model-free spatial resampling 240
10.6.1 A spatial numerical experiment 244
10.6.2 Spatial bootstrap 246
10.7 Unequally spaced observations 246
Bibliography 251
Index 263
回复此楼

» 本帖附件资源列表

» 收录本帖的淘帖专辑推荐

数学分析 自然科学

» 猜你喜欢

» 本主题相关价值贴推荐，对您同样有帮助:
1楼 2016-09-12 19:20:00
已阅   回复此楼   关注TA 给TA发消息 送TA红花 TA的回帖

nudtlxt

新虫 (初入文坛)

★★★★★ 五星级,优秀推荐

非常不错谢谢！
回复此楼
26楼2017-01-16 05:11:53
已阅   回复此楼   关注TA 给TA发消息 送TA红花 TA的回帖
简单回复
applaq2楼
2016-09-12 22:58   回复  
五星好评  顶一下，感谢分享！
gongyongde23楼
2016-09-13 11:17   回复  
五星好评  顶一下，感谢分享！
liuqiang684楼
2016-09-14 06:31   回复  
五星好评  顶一下，感谢分享！
gzbaby5楼
2016-09-14 06:58   回复  
五星好评  顶一下，感谢分享！
anmingkang6楼
2016-09-14 08:51   回复  
五星好评  顶一下，感谢分享！
ha16687楼
2016-09-14 10:19   回复  
五星好评  顶一下，感谢分享！
muchex8楼
2016-09-14 22:28   回复  
五星好评  顶一下，感谢分享！
xcontext9楼
2016-09-15 05:09   回复  
五星好评  顶一下，感谢分享！
xcontext10楼
2016-09-15 05:17   回复  
顶一下，感谢分享！
zhchzhsh207611楼
2016-09-15 21:27   回复  
五星好评  顶一下，感谢分享！
daijzh12楼
2016-09-16 18:25   回复  
五星好评  顶一下，感谢分享！
yuanbing13楼
2016-09-17 16:30   回复  
五星好评  顶一下，感谢分享！
keda14楼
2016-09-18 02:12   回复  
五星好评  顶一下，感谢分享！
derek88815楼
2016-09-18 07:30   回复  
五星好评  顶一下，感谢分享！
788816楼
2016-09-18 09:25   回复  
五星好评  顶一下，感谢分享！
胡不归17楼
2016-09-18 09:46   回复  
五星好评  顶一下，感谢分享！
zhangfanping18楼
2016-09-18 11:07   回复  
五星好评  顶一下，感谢分享！
甲先生19楼
2016-09-26 20:25   回复  
五星好评  顶一下，感谢分享！
yanglishan20楼
2016-09-27 11:11   回复  
五星好评  顶一下，感谢分享！
wangth092121楼
2016-10-07 11:00   回复  
五星好评  顶一下，感谢分享！
昆明大马山22楼
2016-11-10 21:53   回复  
五星好评  感谢分享！
lixinhuacq23楼
2016-11-14 21:26   回复  
五星好评  顶一下，感谢分享！
navolo24楼
2016-11-21 22:19   回复  
五星好评  
navolo25楼
2016-11-21 22:20   回复  
顶一下，感谢分享！
longhuitian198027楼
2017-01-18 21:17   回复  
五星好评  顶一下，感谢分享！
沙漠之狐923028楼
2018-01-24 16:22   回复  
五星好评  顶一下，感谢分享！
parculonew29楼
2019-01-10 17:24   回复  
五星好评  顶一下，感谢分享！
相关版块跳转 我要订阅楼主 pkusiyuan 的主题更新
291/1返回列表
☆ 无星级 ★ 一星级 ★★★ 三星级 ★★★★★ 五星级
普通表情 龙 兔 虎 猫 高级回复 (可上传附件)
信息提示
关闭
请填处理意见
关闭