Search

Your search keyword '"Chen, Ding-Geng"' showing total 1,129 results
Start Over Author "Chen, Ding-Geng"
1,129 results on '"Chen, Ding-Geng"'

1. Emerging Topics in Dose-Finding and Beyond

Author

Ye, Jingjing, Chen, Ding-Geng, Zhou, Wen, Deng, Qiqi, Cappelleri, Joseph C., Chen, Ding-Geng, Series Editor, Ye, Jingjing, editor, Zhou, Wen, editor, Deng, Qiqi, editor, and Cappelleri, Joseph C., editor

Published
2025
Full Text
View/download PDF

8. Semiparametric inference for the scale-mixture of normal partial linear regression model with censored data

Author

Naderi, Mehrdad, Mirfarah, Elham, Bernhardt, Matthew, and Chen, Ding-Geng

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Computation, Statistics - Other Statistics, 62J20, 62E15
Abstract

In the framework of censored data modeling, the classical linear regression model that assumes normally distributed random errors has received increasing attention in recent years, mainly for mathematical and computational convenience. However, practical studies have often criticized this linear regression model due to its sensitivity to departure from the normality and from the partial nonlinearity. This paper proposes to solve these potential issues simultaneously in the context of the partial linear regression model by assuming that the random errors follow a scale-mixture of normal (SMN) family of distributions. The proposed method allows us to model data with great flexibility, accommodating heavy tails, and outliers. By implementing the B-spline function and using the convenient hierarchical representation of the SMN distributions, a computationally analytical EM-type algorithm is developed to perform maximum likelihood inference of the model parameters. Various simulation studies are conducted to investigate the finite sample properties as well as the robustness of the model in dealing with the heavy-tails distributed datasets. Real-word data examples are finally analyzed for illustrating the usefulness of the proposed methodology., Comment: 17 page, 4 figures

Published
2020

9. Mixture of linear experts model for censored data: A novel approach with scale-mixture of normal distributions

Author

Mirfarah, Elham, Naderi, Mehrdad, and Chen, Ding-Geng

Subjects
Statistics - Methodology, Statistics - Applications, Statistics - Computation, 62J05 (Primary), 62E10 (Secondary)
Abstract

The classical mixture of linear experts (MoE) model is one of the widespread statistical frameworks for modeling, classification, and clustering of data. Built on the normality assumption of the error terms for mathematical and computational convenience, the classical MoE model has two challenges: 1) it is sensitive to atypical observations and outliers, and 2) it might produce misleading inferential results for censored data. The paper is then aimed to resolve these two challenges, simultaneously, by proposing a novel robust MoE model for model-based clustering and discriminant censored data with the scale-mixture of normal class of distributions for the unobserved error terms. Based on this novel model, we develop an analytical expectation-maximization (EM) type algorithm to obtain the maximum likelihood parameter estimates. Simulation studies are carried out to examine the performance, effectiveness, and robustness of the proposed methodology. Finally, real data is used to illustrate the superiority of the new model., Comment: 21 pages

Published
2020

10. Estimating Determinants of Stage at Diagnosis of Breast Cancer Prevalence in Western Nigeria Using Bayesian Logistic Regression

Author

Ogunsakin, Ropo Ebenezer, Chen, Ding-Geng (Din), Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, Ng, Hon Keung Tony, Editorial Board Member, Lio, Yuhlong, Editorial Board Member, Manda, Samuel O. M., editor, and Chirwa, Tobias F., editor

Published
2022
Full Text
View/download PDF

11. Bayesian Spatial Modeling of HIV Using Conditional Autoregressive Model

Author

Ogunsakin, Ropo Ebenezer, Chen, Ding-Geng (Din), Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, Ng, Hon Keung Tony, Editorial Board Member, Lio, Yuhlong, Editorial Board Member, Manda, Samuel O. M., editor, and Chirwa, Tobias F., editor

Published
2022
Full Text
View/download PDF

12. Longitudinal Meta-Analysis of Multiple Effect Sizes

Author

Musekiwa, Alfred, Manda, Samuel O. M., Mwambi, Henry G., Chen, Ding-Geng (Din), Abariga, Samuel A., McCaul, Michael, Ochodo, Eleanor, Rohwer, Anke, Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, Ng, Hon Keung Tony, Editorial Board Member, Lio, Yuhlong, Editorial Board Member, Manda, Samuel O. M., editor, and Chirwa, Tobias F., editor

Published
2022
Full Text
View/download PDF

13. Statistical Meta-Analysis and Its Efficiency: A Real Data Analysis and a Monte-Carlo Simulation Study

Author

Chen, Ding-Geng (Din), Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, Ng, Hon Keung Tony, Editorial Board Member, Lio, Yuhlong, Editorial Board Member, Manda, Samuel O. M., editor, and Chirwa, Tobias F., editor

Published
2022
Full Text
View/download PDF

14. Sub-Saharan African Region Strategies to Improve Biostatistics Capacity: Exploring Collaborations Between Training and Research Institutions

Author

Chirwa, Tobias F., Munyewende, Pascalia O., Chen, Ding-Geng (Din), Manda, Samuel O. M., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, Ng, Hon Keung Tony, Editorial Board Member, Lio, Yuhlong, Editorial Board Member, Manda, Samuel O. M., editor, and Chirwa, Tobias F., editor

Published
2022
Full Text
View/download PDF

15. Bayesian Approach for Joint Modeling Longitudinal Data and Survival Data Simultaneously in Public Health Studies

Author

Chen, Ding-Geng, Lio, Yuhlong, Wilson, Jeffrey R., Chen, Ding-Geng, Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, Lio, Yuhlong, editor, Ng, Hon Keung Tony, editor, and Tsai, Tzong-Ru, editor

Published
2022
Full Text
View/download PDF

16. A Competing Risk Model Based on a Two-Parameter Exponential Family Distribution Under Progressive Type II Censoring

Author

Lin, Yu-Jau, Tsai, Tzong-Ru, Chen, Ding-Geng, Lio, Yuhlong, Chen, Ding-Geng, Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, Lio, Yuhlong, editor, Ng, Hon Keung Tony, editor, and Tsai, Tzong-Ru, editor

Published
2022
Full Text
View/download PDF

28. Nonlinear Mixed-Effects Modeling

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

29. The Generalized Linear Model

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

30. Nonlinear Regression Modeling

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

31. Generalized Multi-Level Model for Dichotomous Outcome

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

32. Longitudinal Data Analysis

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

33. Higher-Level Multi-Level Modeling

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

34. Introduction to Multi-Level Modeling

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

35. Linear Regression

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

36. Two-Level Multi-Level Modeling

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

37. Generalized Multi-Level Model for Counts Outcome

Author

Chen, Ding-Geng (Din), Chen, Jenny K., Chen, Ding-Geng (Din), Series Editor, Bekker, Andriëtte, Editorial Board Member, Coelho, Carlos A., Editorial Board Member, Finkelstein, Maxim, Editorial Board Member, Wilson, Jeffrey R., Editorial Board Member, and Chen, Jenny K.

Published
2021
Full Text
View/download PDF

40. Economical Sampling Plans with Warranty

Author

Chiang, Jyun-You, Ng, Hon Keung Tony, Tsai, Tzong-Ru, Lio, Yuhlong, Chen, Ding-Geng, Chen, Jiahua, Series Editor, Chen, Ding-Geng (Din), Series Editor, Lio, Yuhlong, editor, Ng, Hon Keung Tony, editor, Tsai, Tzong-Ru, editor, and Chen, Ding-Geng, editor

Published
2019
Full Text
View/download PDF

42. Modified Cox Models: A Simulation Study on Different Survival Distributions, Censoring Rates, and Sample Sizes.

Author

Maharela, Iketle Aretha, Fletcher, Lizelle, and Chen, Ding-Geng

Subjects
MONTE Carlo method, SURVIVAL analysis (Biometry), SAMPLE size (Statistics), DATA analysis, CENSORSHIP
Abstract

The classical Cox model is the most popular procedure for studying right-censored data in survival analysis. However, it is based on the fundamental assumption of proportional hazards (PH). Modified Cox models, stratified and extended, have been widely employed as solutions when the PH assumption is violated. Nevertheless, prior comparisons of the modified Cox models did not employ comprehensive Monte-Carlo simulations to carry out a comparative analysis between the two models. In this paper, we conducted extensive Monte-Carlo simulation to compare the performance of the stratified and extended Cox models under varying censoring rates, sample sizes, and survival distributions. Our results suggest that the models' performance at varying censoring rates and sample sizes is robust to the distribution of survival times. Thus, their performance under Weibull survival times was comparable to that of exponential survival times. Furthermore, we found that the extended Cox model outperformed other models under every combination of censoring, sample size and survival distribution. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

43. Estimating Time-to-Death and Determining Risk Predictors for Heart Failure Patients: Bayesian AFT Shared Frailty Models with the INLA Method.

Author

Ashine, Tafese, Tadesse Likassa, Habte, and Chen, Ding-Geng

Subjects
HEART failure patients, HEART failure, DISEASE risk factors, CHRONIC kidney failure, ACADEMIC medical centers
Abstract

Heart failure is a major global health concern, especially in Ethiopia. Numerous studies have analyzed heart failure data to inform decision-making, but these often struggle with limitations to accurately capture death dynamics and account for within-cluster dependence and heterogeneity. Addressing these limitations, this study aims to incorporate dependence and analyze heart failure data to estimate survival time and identify risk factors affecting patient survival. The data, obtained from 497 patients at Jimma University Medical Center in Ethiopia were collected between July 2015 and January 2019. Residence was considered as the clustering factor in the analysis. We employed the Bayesian accelerated failure time (AFT), and Bayesian AFT shared gamma frailty models, comparing their performance using the Deviance Information Criterion (DIC) and Watanabe–Akaike Information Criterion (WAIC). The Bayesian log-normal AFT shared gamma frailty model had the lowest DIC and WAIC, with well-capturing cluster dependency that was attributed to unobserved heterogeneity between patient residences. Unlike other methods that use Markov-Chain Monte-Carlo (MCMC), we applied the Integrated Nested Laplace Approximation (INLA) to reduce computational load. The study found that 39.44% of patients died, while 60.56% were censored, with a median survival time of 34 months. Another interesting finding of this study is that adding frailty into the Bayesian AFT models boosted the performance in fitting the heart failure dataset. Significant factors reducing survival time included age, chronic kidney disease, heart failure history, diabetes, heart failure etiology, hypertension, anemia, smoking, and heart failure stage. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

46. Univariate Community Analysis

Author

Xia, Yinglin, Sun, Jun, Chen, Ding-Geng, Chen, Jiahua, Series Editor, Chen, Ding-Geng (Din), Series Editor, Xia, Yinglin, Sun, Jun, and Chen, Ding-Geng

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results