Forthcoming Issue

The following papers are expected to appear in
Volume 28, Number 4, October 2018

Data Missing Not At Random

1. SS-2015-0317 [Supp]
doi:10.5705/ss.202015.0317
A mixed-effects estimating equation approach to nonignorable missing longitudinal data with refreshment samples
Xuan Bi and Annie Qu
2. SS-2015-0437
doi:10.5705/ss.202015.0437
Imputation-based adjusted score equations in generalized linear models with nonignorable missing covariate values
Fang Fang, Jiwei Zhao and Jun Shao
3. SS-2016-0133
doi:10.5705/ss.202016.0133
Sensitivity analysis for unmeasured confounding in coarse structural nested mean models
Shu Yang and Judith Lok
4. SS-2015-0408
doi:10.5705/ss.202015.0408
Calibration and multiple robustness when data are missing not at random
Peisong Han
5. SS-2016-0328
doi:10.5705/ss.202016.0328
Sequential identification of nonignorable missing data mechanisms
Mauricio Sadinle and Jerry Reiter
6. SS-2016-0300 [Supp]
doi:10.5705/ss.202016.0300
Generalization of Heckman selection model to nonignorable nonresponse using call-back information
Baojiang Chen, Pengfei Li and Jing Qin
7. SS-2016-0388
doi:10.5705/ss.202016.0388
Rank-based estimating equation with non-ignorable missing responses via empirical likelihood
Huybrechts F. Bindele and Yichuan Zhao
8. SS-2016-0526
doi:10.5705/ss.202016.0526
Optimal design when outcome values are not missing at random
Kim May Lee, Robin Mitra and Stefanie Biedermann
9. SS-2016-0121
doi:10.5705/ss.202016.0121
Bayesian small area models for three-way contingency tables with nonignorability
Namgyo Woo, Balgobin Nandram and Dalho Kim
10. SS-2016-0350 [Supp]
doi:10.5705/ss.202016.0350
Functional linear regression model for nonignorable missing scalar responses
Tengfei Li, Fengchang Xie, Xiangnan Feng, J. G. Ibrahim and Hongtu Zhu
11. SS-2015-0472 [Supp]
doi:10.5705/ss.202015.0472
Assessment of nonignoralbe log-linear models for an incomplete contingency table
Seongyong Kim and Daeyoung Kim
12. SS-2016-0317 [Supp]
doi:10.5705/ss.202016.0317
A robust calibration-assisted method for linear mixed effects model under cluster-specific nonignorable missingness
Yongchan Kwon, Jae Kwang Kim, Myunghee Paik and Hongsoo Kim
13. SS-2016-0319 [Supp]
doi:10.5705/ss.202016.0319
Bayesian modeling and inference for nonignorably missing longitudinal binary response data with applications to HIV prevention trials
Joseph Ibrahim, Jing Wu, Ming-Hui Chen, Elizabeth Schifano and Jeffrey Fisher
14. SS-2016-0324 [Supp]
doi:10.5705/ss.202016.0324
Semiparametric estimation with data missing not at random using an instrumental variable
Baoluo Sun, Lan Liu, Wang Miao, Kathleen Wirth, James Robins and Eric Tchetgen Tchetgen
15. SS-2016-0308 [Supp]
doi:10.5705/ss.202016.0308
A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials
Ian White, James Carpenter and Nicholas Horton
16. SS-2016-0320
doi:10.5705/ss.202016.0320
Propensity score matching analysis for causal effects with MNAR covariates
Bo Lu and Robert Ashmead
17. SS-2016-0291
doi:10.5705/ss.202016.0291
Empirical likelihood methods for complex surveys with data missing-by-design
Min Chen, Mary Thompson and Changbao Wu
18. SS-2016-0322 [Supp]
doi:10.5705/ss.202016.0322
Identification and inference with nonignorable missing covariate data
Wang Miao, Eric Tchetgen Tchetgen
19. SS-2016-0325 [Supp]
doi:10.5705/ss.202016.0325
Discrete choice models for nonmonotone nonignorable missing data: identification and inference
Eric Tchetgen Tchetgen, Linbo Wang and Baoluo Sun
20. SS-2016-0294
doi:10.5705/ss.202016.0294
Strategic binary choice models with partial observability
Mark Nieman
21. SS-2016-0340 [Supp]
doi:10.5705/ss.202016.0340
Generalized method of moments for nonignorable missing data
Li Zhang, Cunjie Lin and Yong Zhou
22. SS-2016-0312 [Supp]
doi:10.5705/ss.202016.0312
Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data
Jiwei Zhao, Yang Yang and Yang Ning
23. SS-2016-0315
doi:10.5705/ss.202016.0315
Estimation of area under the ROC curve under nonignorable verication bias
Wenbao Yu, Jae Kwang Kim and Taesung Park
24. SS-2017-0016
doi:10.5705/ss.202017.0016
Bayesian inference for nonresponse two-phase sampling
Yue Zhang, Henian Chen and Nanhua Zhang
25. SS-2016-0270
doi:10.5705/ss.202016.0270
Application of non-parametric empirical Bayes to treatment of non-response
Eitan Greenshtein and Theodor Itskov
In Memory of Peter G. Hall
1. SS-2018-0028
doi:10.5705/ss.202018.0028
Peter Gavin Hall
Terry Speed
2. SS-2017-0038
doi:10.5705/ss.202017.0038
Peter Gavin Hall -- A brief remembrance of the man and his work
Francisco J. Samaniego
3. SS-2017-0022
doi:10.5705/ss.202017.0022
Peter Hall: My mentor, collaborator and friend
Peihua Qiu
4. SS-2017-0095
doi:10.5705/ss.202017.0095
Peter Hall on extremes: Research, teaching and supervision
Alan Welsh
5. SS-2016-0393 [Supp]
doi:10.5705/ss.202016.0393
Wavelet methods for erratic regression means in the presence of measurement error
Spiridon Penev, Peter Hall and Jason Tran
6. SS-2016-0416 [Supp]
doi:10.5705/ss.202016.0416
Semi-parametric prediction intervals in small areas when auxiliary data are measured with error
Gauri Datta, Aurore Delaigle, Peter Gavin Hall and Lily Wang
7. SS-2017-0093 [Supp]
doi:10.5705/ss.202017.0093
Clustering in general measurement error models
Raymond Carroll, Ya Su and Jill Reedy
8. SS-2017-0101 [Supp]
doi:10.5705/ss.202017.0101
Estimation of errors-in-variables partially linear additive models
Byeong Park, Eun Ryung Lee and Kyunghee Han
9. SS-2017-0059
doi:10.5705/ss.202017.0059
Peter Hall's contribution to empirical likelihood
Jinyuan Chang, Jianjun Guo and Cheng Yong Tang
10. SS-2017-0291 [Supp]
doi:10.5705/ss.202017.0291
Hybrid combinations of parametric and empirical likelihoods
Nils Lid Hjort, Ian W. McKeague and Ingrid Van Keilegom
11. SS-2017-0041 [Supp]
doi:10.5705/ss.202017.0041
Empirical likelihood ratio tests for coefficients in high dimensional heteroscedastic linear models
Honglang Wang, Ping-Shou Zhong and Yuehua Cui
12. SS-2016-0537 [Supp]
doi:10.5705/ss.202016.0537
An outlyingness matrix for multivariate functional data classification
Wenlin Dai and Marc G. Genton
13. SS-2017-0099 [Supp]
doi:10.5705/ss.202017.0099
Adaptive functional linear regression via functional principal component analysis and block thresholding
T. Tony Cai, Linjun Zhang and Harrison H. Zhou
14. SS-2017-0199 [Supp]
doi:10.5705/ss.202017.0199
Functional principal component analysis for derivatives of multivariate curves
Maria Grith, Heiko Wagner,Wolfgang K. Hardle and Alois Kneip
15. SS-2016-0556 [Supp]
doi:10.5705/ss.202016.0556
Singular additive models for function to function regression
Byeong U. Park, Chun-Jui Chen, Wenwen Tao and Hans-Georg Muller
16. SS-2016-0536 [Supp]
doi:10.5705/ss.202016.0536
Methodology and convergence rates for functional time series regression
Tung Pham and Victor M. Panaretos
17. SS-2017-0296 [Supp]
doi:10.5705/ss.202017.0296
Edgeworth correction for the largest eigenvalue in a spiked PCA model
Jeha Yang and Iain M. Johnstone
18. SS-2016-0546
doi:10.5705/ss.202016.0546
Calibrated percentile double bootstrap for robust linear regression inference
Kai Zhang, Daniel McCarthy, Lawrence Brown, Richard Berk, Andreas Buja, Edward George and Linda Zhao
19. SS-2017-0121 [Supp]
doi:10.5705/ss.202017.0121
Edgeworth expansions for a class of spectral density estimators and their applications to interval estimation
Arindam Chatterjee and Soumendra Lahiri
20. SS-2017-0013 [Supp]
doi:10.5705/ss.202017.0013
A bootstrap method for constructing pointwise and uniform confidence bands for conditional quantile functions
Joel L. Horowitz and Anand Krishnamurthy
21. SS-2017-0027 [Supp]
doi:10.5705/ss.202017.0027
Partial consistency with sparse incidental parameters
Jianqing Fan, Runlong Tang and Xiaofeng Shi
22. SS-2017-0060 [Supp]
doi:10.5705/ss.202017.0060
Applications of Peter Hall's martingale limit theory to estimating and testing high dimensional covariance matrices
Danning Li, Lingzhou Xue and Hui Zou
23. SS-2017-0213
doi:10.5705/ss.202017.0213
High-dimensional two-sample covariance matrix testing via super-diagonals
Jing He and Song Xi Chen
24. SS-2017-0344 [Supp]
doi:10.5705/ss.202017.0344
Estimating a discrete log-concave distribution in higher dimensions
Hanna Jankowski and Amanda Tian
25. SS-2016-0401 [Supp]
doi:10.5705/ss.202016.0401
Asymptotic behavior of Cox's partial likelihood and its application to variable selection
Runze Li, Jian-Jian Ren, Guangren Yang and Ye Yu
26. SS-2017-0056 [Supp]
doi:10.5705/ss.202017.0056
Data sharpening guided by global constraint in local regression
W. John Braun, X. Joan Hu and Xiuli Kang
27. SS-2017-0058 [Supp]
doi:10.5705/ss.202017.0058
Bias reduction for nonparametric and semiparametric regression models
Ming-Yen Cheng, Tao Huang, Peng Liu and Heng Peng
28. SS-2016-0369
doi:10.5705/ss.202016.0369
Nonlinear regression estimation using subset-based kernel principal components
Yuan Ke, Degui Li and Qiwei Yao
29. SS-2017-0034 [Supp]
doi:10.5705/ss.202017.0034
Optimal model averaging of varying coefficient models
Cong Li, Qi Li, Jeffrey S. Racine and Daiqiang Zhang
30. SS-2017-0057 [Supp]
doi:10.5705/ss.202017.0057
Empirical Fourier methods for interval censored data
Peter G. Hall, John Braun and Thierry Duchesne
31. SS-2016-0507
doi:10.5705/ss.202016.0507
On p-values
Laurie Davies
32. SS-2016-0538 [Supp]
doi:10.5705/ss.202016.0538
Kernel-based adaptive randomization toward balance in continuous and discrete covariates
Yanyuan Ma, Fei Jiang and Guosheng Yin
33. SS-2016-0497 [Supp]
doi:10.5705/ss.202016.0497
Tests for tar models vs. Star models--a separate family of hypotheses approach
Zhaoxing Gao, Shiqing Ling and Howell Tong
34. SS-2017-0029 [Supp]
doi:10.5705/ss.202017.0029
Likelihood ratio Haar variance stabilization and normalization for Poisson and other non-Gaussian noise removal
Piotr Fryzlewicz
35. SS-2017-0043
doi:10.5705/ss.202017.0043
RFMS method for credit scoring based on bank card transaction data
Jing Zhou, Danyang Huang and Hansheng Wang