Japanese Journal of Statistics and Data Science

Công bố khoa học tiêu biểu

* Dữ liệu chỉ mang tính chất tham khảo

Sắp xếp:  
Special feature: computational statistics and machine learning
Japanese Journal of Statistics and Data Science - Tập 2 - Trang 219-220 - 2019
Hiroshi Yadohisa, Wataru Sakamoto
Consistency of test-based method for selection of variables in high-dimensional two-group discriminant analysis
Japanese Journal of Statistics and Data Science - Tập 2 - Trang 155-171 - 2019
Yasunori Fujikoshi, Tetsuro Sakurai
This paper is concerned with selection of variables in two-group discriminant analysis with the same covariance matrix. We propose a test-based method (TM) drawing on the significance of each variable. Sufficient conditions for the test-based method to be consistent are provided when the dimension and the sample size are large. For the case that the dimension is larger than the sample size, a ridg... hiện toàn bộ
Exact variation and drift parameter estimation for the nonlinear fractional stochastic heat equation
Japanese Journal of Statistics and Data Science - - 2023
Julie Gamain, Ciprian A. Tudor
CHEMIST: an R package for causal inference with high-dimensional error-prone covariates and misclassified treatments
Japanese Journal of Statistics and Data Science - - Trang 1-17 - 2023
Li-Pang Chen, Wei-Hsin Hsu
In this paper, we study causal inference with complex and noisy data accommodated. A new structure is called CHEMIST, which refers to Causal inference with High-dimensional Error-prone covariates and MISclassified Treatments. To suitably tackle those challenges when estimating the average treatment effect (ATE), we develop the FATE method, which reflects Feature screening, Adaptive lasso, Treatmen... hiện toàn bộ
Least-squares estimators based on the Adams method for stochastic differential equations with small Lévy noise
Japanese Journal of Statistics and Data Science - Tập 5 - Trang 217-240 - 2022
Mitsuki Kobayashi, Yasutaka Shimizu
We consider stochastic differential equations (SDEs) driven by small Lévy noise with some unknown parameters and propose a new type of least-squares estimators based on discrete samples from the SDEs. To approximate the increments of a process from the SDEs, we shall use not the usual Euler method but the Adams method, that is, a well-known numerical approximation of the solution to the ordinary d... hiện toàn bộ
Estimation and classification using progressive type-II censored samples from two exponential populations with a common location
Japanese Journal of Statistics and Data Science - Tập 6 - Trang 243-278 - 2023
Pushkal Kumar, Manas Ranjan Tripathy
This article considers the problems of estimation and classification using progressive type-II censored samples as training data from two exponential populations with a common location and different scale parameters. First, we derive improved estimators over the maximum likelihood estimator (MLE) and uniformly minimum variance unbiased estimator (UMVUE) of the common location parameter with and wi... hiện toàn bộ
Penalized least squares approximation methods and their applications to stochastic processes
Japanese Journal of Statistics and Data Science - Tập 3 - Trang 513-541 - 2020
Takumi Suzuki, Nakahiro Yoshida
We construct an objective function that consists of a quadratic approximation term and an $$L^q$$ penalty $$(0... hiện toàn bộ
The XGTDL family of survival distributions
Japanese Journal of Statistics and Data Science - Tập 4 - Trang 1227-1245 - 2021
Gilbert MacKenzie, Miliça Blagojevic-Bucknall, Yasin Al-tawarah, Defen Peng
Non-PH parametric survival modelling is developed within the framework of the multiple logistic function. The family considered comprises three basic models: (a) a PH model, (b) an accelerated life model and (c) a model which is non-proportional hazards and non-accelerated life. The last model, the generalised time-dependent logistic model was described first by the author in 1996 and this model g... hiện toàn bộ
Forward variable selection for sparse ultra-high-dimensional generalized varying coefficient models
Japanese Journal of Statistics and Data Science - Tập 4 - Trang 151-179 - 2020
Toshio Honda, Chien-Tong Lin
In this paper, we propose forward variable selection procedures for feature screening in ultra-high-dimensional generalized varying coefficient models. We employ regression spline to approximate coefficient functions and then maximize the log-likelihood to select an additional relevant covariate sequentially. If we decide we do not significantly improve the log-likelihood any more by selecting any... hiện toàn bộ
Variance estimation procedures in the presence of singly imputed survey data: a critical review
Japanese Journal of Statistics and Data Science - Tập 3 - Trang 583-623 - 2020
David Haziza, Audrey-Anne Vallée
The problem of variance estimation in the presence of singly imputed data has attracted a lot of attention in the last three decades. Treating the imputed values as if they were observed may result in serious underestimation of the true variance of point estimates, leading to invalid inferences. In this paper, we review the approaches/methods proposed in the literature for obtaining variance estim... hiện toàn bộ
Tổng số: 153   
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 10