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ORCIDComputational Statistics & Data Analysis, Volume 114
Volume 114, October 2017
- Justine Shults:
Simulating longer vectors of correlated binary random variables via multinomial sampling. 1-11 - Philip L. H. Yu
, Xiaohang Wang, Yuanyuan Zhu:
High dimensional covariance matrix estimation by penalizing the matrix-logarithm transformed likelihood. 12-25 - Marcos Carzolio, Scotland Leman:
Weighted particle tempering. 26-37 - Ivan Gorynin, Stéphane Derrode, Emmanuel Monfrini, Wojciech Pieczynski:
Fast smoothing in switching approximations of non-linear and non-Gaussian models. 38-46
- Shonosuke Sugasawa, Tatsuya Kubokawa:
Transforming response values in small area prediction. 47-60 - Jong-June Jeon, Sunghoon Kwon, Hosik Choi:
Homogeneity detection for the high-dimensional generalized linear model. 61-74 - Adrian J. Baddeley, Andrew Hardegen, Thomas Lawrence, Robin K. Milne, Gopalan M. Nair, Suman Rakshit:
On two-stage Monte Carlo tests of composite hypotheses. 75-87 - Xingxiang Li, Guosheng Cheng, Liming Wang, Peng Lai, Fengli Song:
Ultrahigh dimensional feature screening via projection. 88-104 - Chang Yu, Daniel Zelterman:
A parametric model to estimate the proportion from true null using a distribution for p-values. 105-118 - Shyamsundar Sahoo, Debasis Sengupta:
Testing the hypothesis of increasing hazard ratio in two samples. 119-129 - Matthieu Marbac, Mohammed Sedki:
A family of block-wise one-factor distributions for modeling high-dimensional binary data. 130-145 - Chunlin Wang, Paul Marriott, Pengfei Li:
Testing homogeneity for multiple nonnegative distributions with excess zero observations. 146-157
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