Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Chew-Seng Chee"'
Autor:
Chew-Seng Chee
Publikováno v:
Statistika, Vol 6, Iss 2 (2014)
The main aim of this paper is to investigate whether there is evidence to suggest that the underlying trend of the pattern of Central England Temperature (CET) series varies over time. In addition, it is also of particular interest in describing the
Externí odkaz:
https://doaj.org/article/9736996b748241eab693b5a1021431ca
Autor:
Chew-Seng Chee, Byungtae Seo
Publikováno v:
Statistics and Computing. 32
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::894b72f1a21660a4de80cbb8e23203e4
https://doi.org/10.1007/s11222-022-10150-x
https://doi.org/10.1007/s11222-022-10150-x
Publikováno v:
Statistical methods in medical research. 30(11)
A consequence of using a parametric frailty model with nonparametric baseline hazard for analyzing clustered time-to-event data is that its regression coefficient estimates could be sensitive to the underlying frailty distribution. Recently, there ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51d4147d76fbbe00cc2c062ca135a7be
https://pubmed.ncbi.nlm.nih.gov/34569366
https://pubmed.ncbi.nlm.nih.gov/34569366
Autor:
Chew-Seng Chee
Publikováno v:
Computational Statistics & Data Analysis. 109:34-44
The density of a mixture distribution with unknown discrete mixing distribution can be a way of finding a nonparametric estimate of a density. Comparing with a standard parametric approach that runs the risk of model misspecification and a kernel-bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::36d3bf8ec4daddb3860d7020870485c6
https://doi.org/10.1016/j.csda.2016.11.012
https://doi.org/10.1016/j.csda.2016.11.012
Autor:
Chew-Seng Chee, Yong Wang
Publikováno v:
Computational Statistics & Data Analysis. 93:107-118
Nonparametric mixture models are commonly used to estimate the number of unobserved species for their robustness of modelling heterogeneity. In particular, Poisson mixtures are popular in this regard, but they are also known to have the boundary prob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33662a0138bc80f1e857922ff83eab48
https://doi.org/10.1016/j.csda.2014.10.021
https://doi.org/10.1016/j.csda.2014.10.021
Autor:
Byungtae Seo, Chew-Seng Chee
Publikováno v:
Computational Statistics & Data Analysis. 152:107053
To avoid the effect of distributional misspecification in the model-based regression, we propose an essentially nonparametric symmetric error distribution and construct a so-called doubly smoothed (DS) likelihood function by applying the same amount
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::701aad8330f29b34dad523880b6a1721
https://doi.org/10.1016/j.csda.2020.107053
https://doi.org/10.1016/j.csda.2020.107053
Autor:
Chew-Seng Chee
Publikováno v:
AStA Advances in Statistical Analysis. 100:239-257
Nonparametric modelling of count data is partly motivated by the fact that using parametric count models not only runs the risk of model misspecification but also is rather restrictive in terms of local approximation. Accordingly, we present a framew
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7699e2e403884f6d29b01ea426d6175c
https://doi.org/10.1007/s10182-015-0255-7
https://doi.org/10.1007/s10182-015-0255-7
Autor:
Yong Wang, Chew-Seng Chee
Publikováno v:
Computational Statistics & Data Analysis. 74:209-216
The fact that a k-monotone density can be defined by means of a mixing distribution makes its estimation feasible within the framework of mixture models. It turns the problem naturally into estimating a mixing distribution, nonparametrically. This pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::674670b0dfd11b72d84f315455b55b68
https://doi.org/10.1016/j.csda.2014.01.007
https://doi.org/10.1016/j.csda.2014.01.007
Autor:
Chew-Seng Chee, Yong Wang
Publikováno v:
Computational Statistics & Data Analysis. 57:1-16
Quadratic loss is predominantly used in the literature as the performance measure for nonparametric density estimation, while nonparametric mixture models have been studied and estimated almost exclusively via the maximum likelihood approach. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67a3682a387c22c10d41c827efa5f2b2
https://doi.org/10.1016/j.csda.2012.06.004
https://doi.org/10.1016/j.csda.2012.06.004
Autor:
Yong Wang, Chew-Seng Chee
Publikováno v:
Statistical Modelling. 12:67-92
This article presents a general framework for univariate non-parametric density estimation, based on mixture models. Similar to kernel-based estimation, the proposed approach uses bandwidth to control the density smoothness, but each density estimate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea89647adc2551e3e438edb8491c28e3
https://doi.org/10.1177/1471082x1001200104
https://doi.org/10.1177/1471082x1001200104