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On a General Class of semiparametric Hazards Regression Models for Recurrent Gap Times

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Acta Mathematicae Applicatae Sinica 2019年第3期35卷 549-563页

作者： Qin JIANG Hui ZHAO Hong QIN Department of Statistics Central China Normal UniversityWuhan 430079China Department of Mathematics Huanggang Normal UniversityHuanggang 438000China Department of Statistics Zhongnan University of Economics and LawWuhan 430073China

In the article,we investigate a general class of semiparametric hazards regression models for recurrent gap *** general class includes the proportional hazards model,the accelerated failure time model and the accelerated hazards models as special *** model is flexible in modelling recurrent gap times since a covariate effect is identified as having two separate components,namely a time-scale change on hazard progression and a relative hazards *** order to infer the model parameters,the procedure is proposed based on estimating *** asymptotic properties of the proposed estimators are established and the finite sample properties are investigated via simulation *** addition,a lack of fit test is presented to assess the adequacy of the model and an application of data from a bladder cancer study is reported for illustration.

关键词： gap times model checking recurrent events estimating equations semiparametric model

ESTIMATION THEORY OF A CLASS OF semiparametric REGRESSION MODELS

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Science China Mathematics 1992年第6期35卷 657-674页

作者：洪圣岩 Department of Mathematics Anhui Univertity Hefei 230039 PRC

Consider the semiparametric regression model Y=X'β+ g(T) + e, where (X,T) is R^p×[0,1]-valued random variables, βa p×1 vector of unknown parameter, g an unknown smoothfunction of T in [0,1], e the random error with mean 0 and variance σ~2>0, possiblyunknown. Assume that e and (X,T) are independent. In this paper, the estimatots ?, g_n~* and? of β,g and σ~2, respectively, based on the combination of nearest neighbor rule and leastsquare rule, are studied. The asymptotic normalities of ? and ? and tbe optimal con-vergence rate of g_n~* are obtained under suitable conditions.

关键词： semiparametric regression model nearest neighbor rule asymptotic normality optimal convergence rate.

Iterative weighted partial spline least squares estimation in semiparametric modeling of longitudinal data

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Science China Mathematics 2003年第5期46卷 724-735页

作者：孙孝前尤进红 Department of Mathematics Huaiyin Teachers College Huaian 223001 China Department of Statistics University of Missouri-Columbia Columbia MO 65211 USA Department of Mathematics and Statistics University of Regina Regina Saskatchewan Canada S4S 0A2

In this paper we consider the estimating problem of a semiparametric regression modelling whenthe data are longitudinal. An iterative weighted partial spline least squares estimator (IWPSLSE) for the para-metric component is proposed which is more efficient than the weighted partial spline least squares estimator(WPSLSE) with weights constructed by using the within-group partial spline least squares residuals in the senseof asymptotic variance. The asymptotic normality of this IWPSLSE is established. An adaptive procedure ispresented which ensures that the iterative process stops after a finite number of iterations and produces anestimator asymptotically equivalent to the best estimator that can be obtained by using the iterative proce-dure. These results are generalizations of those in heteroscedastic linear model to the case of semiparametric regression.

关键词： semiparametric modelling, longitudinal data, iterative weighted partial spline leastsquares estimator (IWPSLSE), asymptotic normality.

Some Results on Asymptotic Efficiency of Estimators in semiparametric Models

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Chinese Science Bulletin 1993年第8期38卷 617-620页

作者：梁华成平 Institute of Systems Science Academia Sinica

Generalized Ridge Estimation of a semiparametric Regression Model

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Wuhan University Journal of Natural Sciences 2010年第4期15卷 283-286页

作者： Hongchang Hu Shaolin Rao School of Mathematics and Statistics Hubei Normal University Huangshi Hubei China

We considered the following semiparametric regres-sion model yi = X iT β+ s ( t i ) + ei (i =1,2,,n). First,the general-ized ridge estimators of both parameters and non-parameters are given without a restrained design matrix. Second,the generalized ridge estimator will be compared with the penalized least squares estimator under a mean squares error,and some conditions in which the former excels the latter are given. Finally,the validity and feasibility of the method is illustrated by a simulation example.

关键词： semiparametric regression model generalized ridge estimation penalized least squares estimation mean squares error

semiparametric Estimation of Multivariate GARCH Models

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Open Journal of Statistics 2015年第7期5卷 852-858页

作者： Claudio Morana Department of Economics Management and Statistics University of Milan-Bicocca Milan Italy Center for Research on Pensions and Welfare Policies Collegio Carlo Alberto Moncalieri Italy Rimini Center for Economic Analysis Rimini Italy

The paper introduces a new simple semiparametric estimator of the conditional variance-covariance and correlation matrix (SP-DCC). While sharing a similar sequential approach to existing dynamic conditional correlation (DCC) methods, SP-DCC has the advantage of not requiring the direct parameterization of the conditional covariance or correlation processes, therefore also avoiding any assumption on their long-run target. In the proposed framework, conditional variances are estimated by univariate GARCH models, for actual and suitably transformed series, in the first step;the latter are then nonlinearly combined in the second step, according to basic properties of the covariance and correlation operator, to yield nonparametric estimates of the various conditional covariances and correlations. Moreover, in contrast to available DCC methods, SP-DCC allows for straightforward estimation also for the non-symultaneous case, i.e. for the estimation of conditional cross-covariances and correlations, displaced at any time horizon of interest. A simple expost procedure to ensure well behaved conditional variance-covariance and correlation matrices, grounded on nonlinear shrinkage, is finally proposed. Due to its sequential implementation and scant computational burden, SP-DCC is very simple to apply and suitable for the modeling of vast sets of conditionally heteroskedastic time series.

关键词： Multivariate GARCH Model Dynamic Conditional Correlation semiparametric Estimation

ON B-SPLINE M-ESTIMATORS IN A semiparametric REGRESSION MODEL

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Systems Science and Mathematical Sciences 1994年第3期7卷 270-281页

作者： SHI Peide (Department of Probability and Statistics, Peking University, Beijng 100871, China) 北京大学概率统计学系北京 100871

ＯＮＢ－ＳＰＬＩＮＥＭ－ＥＳＴＩＭＡＴＯＲＳＩＮＡＳＥＭＩＰＡＲＡＭＥＴＲＩＣＲＥＧＲＥＳＳＩＯＮＭＯＤＥＬ￥ＳＨＩＰｅｉｄｅ（ＤｅｐａｒｔｍｅｎｔｏｆＰｒｏｂａｂｉｌｉｔｙａｎｄＳｔａｔｉｓｔｉｃｓ，ＰｅｋｉｎｇＵ... 详细信息

ＯＮＢ－ＳＰＬＩＮＥＭ－ＥＳＴＩＭＡＴＯＲＳＩＮＡＳＥＭＩＰＡＲＡＭＥＴＲＩＣＲＥＧＲＥＳＳＩＯＮＭＯＤＥＬ￥ＳＨＩＰｅｉｄｅ（ＤｅｐａｒｔｍｅｎｔｏｆＰｒｏｂａｂｉｌｉｔｙａｎｄＳｔａｔｉｓｔｉｃｓ，ＰｅｋｉｎｇＵｎｉｖｅｒｓｉｔｙ，Ｂｅｉｊｎｇ１...

关键词： semiparametric regression model M-estimator optical global rate of convergence B-spline function.

ON　THE　SMALLEST　POSSIBLE　ASYMPTOTICALLY　EFFICIENT　VARIANCE　IN　semiparametric　MODELS

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Systems Science and Mathematical Sciences 1994年第1期7卷 29-33页

作者： LIANG Hua(Institute Of Systems Science, Academia Sinica, Beliing 100080, China) 中科院系统科学所北京 100080

ＯＮＴＨＥＳＭＡＬＬＥＳＴＰＯＳＳＩＢＬＥＡＳＹＭＰＴＯＴＩＣＡＬＬＹＥＦＦＩＣＩＥＮＴＶＡＲＩＡＮＣＥＩＮＳＥＭＩＰＡＲＡＭＥＴＲＩＣＭＯＤＥＬＳ￥ＬＩＡＮＧＨｕａ（ＩｎｓｔｉｔｕｔｅＯｆＳｙｓｔｅｍｓＳｃｉｅｎｃ... 详细信息

ＯＮＴＨＥＳＭＡＬＬＥＳＴＰＯＳＳＩＢＬＥＡＳＹＭＰＴＯＴＩＣＡＬＬＹＥＦＦＩＣＩＥＮＴＶＡＲＩＡＮＣＥＩＮＳＥＭＩＰＡＲＡＭＥＴＲＩＣＭＯＤＥＬＳ￥ＬＩＡＮＧＨｕａ（ＩｎｓｔｉｔｕｔｅＯｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ＡｃａｄｅｍｉａＳｉｎｉｃａ，...

关键词： semiparametric models the smallest possible asymptotically efficient variance.

A Simulation Study on Comparing General Class of semiparametric Transformation Models for Survival Outcome with Time-Varying Coefficients and Covariates

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Open Journal of Statistics 2019年第2期9卷 169-180页

作者： Yemane Hailu Fissuh Tsegay Giday Woldu Idriss Abdelmajid Idriss Ahmed Abebe Zewdie Kebebe Department of Statistics College of Applied Sciences Beijing University of Technology Beijing China Department of Statistics College of Natural and Computational Sciences Aksum University Aksum Ethiopia Department of Mathematics College of Applied Sciences Beijing University of Technology Beijing China School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing China

The consideration of the time-varying covariate and time-varying coefficient effect in survival models are plausible and robust techniques. Such kind of analysis can be carried out with a general class of semiparametric transformation models. The aim of this article is to develop modified estimating equations under semiparametric transformation models of survival time with time-varying coefficient effect and time-varying continuous covariates. For this, it is important to organize the data in a counting process style and transform the time with standard transformation classes which shall be applied in this article. In the situation when the effect of coefficient and covariates change over time, the widely used maximum likelihood estimation method becomes more complex and burdensome in estimating consistent estimates. To overcome this problem, alternatively, the modified estimating equations were applied to estimate the unknown parameters and unspecified monotone transformation functions. The estimating equations were modified to incorporate the time-varying effect in both coefficient and covariates. The performance of the proposed methods is tested through a simulation study. To sum up the study, the effect of possibly time-varying covariates and time-varying coefficients was evaluated in some special cases of semiparametric transformation models. Finally, the results have shown that the role of the time-varying covariate in the semiparametric transformation models was plausible and credible.

关键词： Estimating Equation semiparametric Transformation Models Time-to-Event Outcomes Time-Varying Coefficients Time-Varying Covariate