Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Xing-Fang Huang"'
Publikováno v:
Risks, Vol 5, Iss 1, p 14 (2017)
This paper considered a dependent discrete-time risk model, in which the insurance risks are represented by a sequence of independent and identically distributed real-valued random variables with a common Gamma-like tailed distribution; the financia
Externí odkaz:
https://doaj.org/article/37576d3c99914b328a0780c08fc1000d
Publikováno v:
Nonlinear Analysis, Vol 23, Iss 2 (2018)
Let {Xi , i ⩾ 1} be a sequence of identically distributed real-valued random variables with common distribution FX; let {θi , i ⩾ 1} be a sequence of identically distributed, nonnegative and nondegenerate at zero random variables; and let τ be
Externí odkaz:
https://doaj.org/article/685146f781c24ad7a351421456da7277
Publikováno v:
Expert Systems with Applications. 207:117951
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::387d95d0d8c0884dd63e49ce3673cc51
https://doi.org/10.1016/j.eswa.2022.117951
https://doi.org/10.1016/j.eswa.2022.117951
Autor:
Dan Xiao, Meng-Ting Zheng, Fang-Juan Wu, Xiao-Xia Cao, Xing-Fang Huang, Li Huang, Xue-Qing Xiao
Publikováno v:
Polymer Degradation and Stability. 203:110060
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2cda0b15426bb9d5bf927d7ace6f41e
https://doi.org/10.1016/j.polymdegradstab.2022.110060
https://doi.org/10.1016/j.polymdegradstab.2022.110060
Publikováno v:
Communications in Statistics - Simulation and Computation. 48:2599-2614
The estimation of variance function plays an extremely important role in statistical inference of the regression models. In this paper we propose a variance modelling method for constructin...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82c5f430883bbd01a53e765a17e70434
https://doi.org/10.1080/03610918.2018.1458136
https://doi.org/10.1080/03610918.2018.1458136
Publikováno v:
Applied Stochastic Models in Business and Industry. 34:355-375
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5382a0b340fdd6f9b1d49041daf485d9
https://doi.org/10.1002/asmb.2305
https://doi.org/10.1002/asmb.2305
Publikováno v:
TEST. 26:574-599
Time-varying coefficient models are very important tools to explore the hidden structure between the response variable and its predictors. In some applications, the coefficient curves have singularities, including jump points at some unknown position
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cfa461f491f79e2670f1900f1387b1c5
https://doi.org/10.1007/s11749-017-0525-7
https://doi.org/10.1007/s11749-017-0525-7
Publikováno v:
Statistics & Probability Letters. 121:12-17
If certain knowledge is available, then additional factors can be studied through designs containing partially clear main effects. Three types are found less useful, and only the fourth type can overcome the drawback. The existence and constructions
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1029d0055a6a9d400221f0ded73ee862
https://doi.org/10.1016/j.spl.2016.09.012
https://doi.org/10.1016/j.spl.2016.09.012
Publikováno v:
Journal of the Korean Statistical Society. 45:595-609
Influence analysis has become an important tool for statistical analysis. This paper is concerned with Bayesian case influence analysis for generalized autoregressive conditional heteroscedasticity (GARCH) model. Case influence analysis is developed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84c8c7baabab9eeb4ffe2cff3553e9ba
https://doi.org/10.1016/j.jkss.2016.05.002
https://doi.org/10.1016/j.jkss.2016.05.002
Publikováno v:
Communications in Statistics - Simulation and Computation. 46:6283-6297
Nonparametric models with jump points have been considered by many researchers. However, most existing methods based on least squares or likelihood are sensitive when there are outliers or the erro...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d801dec2a23671c49a6ad3868855180
https://doi.org/10.1080/03610918.2016.1202272
https://doi.org/10.1080/03610918.2016.1202272