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 Professor Peter 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 |