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pro vyhledávání: '"Jayabrata Biswas"'
Autor:
Kiranmoy Das, Jayabrata Biswas
Publikováno v:
Computational Statistics. 36:241-260
Quantile regression is a powerful tool for modeling non-Gaussian data, and also for modeling different quantiles of the probability distributions of the responses. We propose a Bayesian approach of estimating the quantiles of multivariate longitudina
A semi-parametric quantile regression approach to zero-inflated and incomplete longitudinal outcomes
Publikováno v:
AStA Advances in Statistical Analysis. 104:261-283
Quantile regression models are typically used for modeling non-Gaussian outcomes, and such models allow quantile-specific inference. While there exists a vast literature on conditional quantile regression (where the model parameters are estimated pre
Autor:
Jayabrata Biswas, Kiranmoy Das
Publikováno v:
Statistical Modelling. 20:148-170
There is a rich literature on the analysis of longitudinal data with missing values. However, the analysis becomes complex for semi-continuous (zero-inflated) longitudinal response with missingness. In this article, we propose a partially varying coe
Publikováno v:
AStA Advances in Statistical Analysis. 103:453-473
Complexity of longitudinal data lies in the inherent dependence among measurements from same subject over different time points. For multiple longitudinal responses, the problem is challenging due to inter-trait and intra-trait dependence. While line
Publikováno v:
International Journal of Communication Systems. 33:e4390
Abstract: Two-stage regression methods are typically used for handling endogeneity in the simultaneous equations models in economics and other social sciences. However, the problem is challenging in the presence of incomplete response and/or incomple
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff49f967b51dfcf14156d12a03d008c8