Partially linear single-index models for longitudinal data
We fit the data with PLSiM by using the standardized continuous variables as the single index components, including X 1 = LEV ERAGE, X 2 = ASSETMAT, X 3 = MV / BV, X 4 = SIZE, X 5 = CHNGEEPS, X 6 = GTAXRATE, X 7 = BTAXRATE, X 8 = V AR, and X 9 = TER M. In addition, the two dummy variables, LOWBOND and HIGHBOND, are the linear components, i.e., Z 1 = LOWBOND and Z 2 = HIGHBOND. Downloadable! In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on the combination of the local linear smoothing and generalized estimation equations (GEE) is introduced to estimate the two parameter vectors as well as the We consider marginal generalized partially linear single-index models for longitudinal data. A profile generalized estimating equations (GEE)-based approac Within a wide range of bandwidths for estimating the nonparametric function, our profile GEE estimator is consistent and asymptotically normal even if the covariance structure is misspecified. In this article, a partially linear single-index model for longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered.
We consider a so called generalized partially linear model including random effects in we can find applications of linear random effect models in the analysis of longitudinal data sets generalized partially linear single-index models. E[Y |X
Longitudinal data analysis, Cox models, Local partial like- lihood. 1. 1991), the single index models (Härdle and Stoker, 1990) and others. There, the model Reference [2] and [3] studied the partially linear varying coefficient (PLVC) model and single index model, respectively, for longitudinal data. For longitudinal One of the most common approaches to analyze longitudinal data is by assuming marginal models; see Pepe and Anderson (1994). Consider the marginal longitudinal generalized partially linear single-index model (2) E E β γ for and . Besides, is assumed to be a known monotonic and differentiable link function. Liang and Tsai (2014) considered a partially linear single-index longitudinal data model by using polynomial splines to approximate the unknown link function, but their discussion was limited to the sparse and balanced lon-gitudinal data case. In contrast, as mentioned in Section 1, our framework
A nonparametric dynamic additive regression model for longitudinal data Martinussen, Torben and Scheike, Thomas H., The Annals of Statistics, 2000; Fused kernel-spline smoothing for repeatedly measured outcomes in a generalized partially linear model with functional single index Jiang, Fei, Ma, Yanyuan, and Wang, Yuanjia, The Annals of Statistics, 2015
We consider a so called generalized partially linear model including random effects in we can find applications of linear random effect models in the analysis of longitudinal data sets generalized partially linear single-index models. E[Y |X 22 Nov 2007 Estimation in a semiparametric model for longitudinal data with likelihood confidence regions in a partially linear single-index model. This article proposes a partial autoregression single index model to combine network structure inferred longitudinal data with partial linear single index model.
Liang and Tsai (2014) considered a partially linear single-index longitudinal data model by using polynomial splines to approximate the unknown link function, but their discussion was limited to the sparse and balanced lon-gitudinal data case. In contrast, as mentioned in Section 1, our framework
28 May 2018 both the parameters and the nonparametric link function in partially linear single‐index models for longitudinal data that may be unbalanced. We study generalized partially linear single-index models for longitudinal data in this article. We propose a method to efficiently estimate both the parameters Yet, the technique cannot be directly applied to partially linear single-index models for longitudinal data due to the within-subject correlation. In this paper, a In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense 12 Oct 2015 We consider marginal generalized partially linear single-index models for longitudinal data. A profile generalized estimating equations In this paper, we study the estimation for a partial-linear single-index model. A two-stage estimation procedure is proposed to estimate the link function for the
Abstract. In this paper, we consider the partially linear single-index models with longitudinal data. To deal with the variable selection problem in this context, we propose a penalized procedure combined with two bias correction methods, resulting in the bias-corrected generalized estimating equation and the bias-corrected quadratic inference function, which can take into account the
FUNCTIONAL SINGLE INDEX MODELS FOR LONGITUDINAL DATA By Ci-Ren JiangandJane-LingWang1 University of California, Berkeley and University of California, Davis A new single-index model that reflects the time-dynamic effects of the single index is proposed for longitudinal and functional response data, possibly measured with errors, for both In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on a combination of the local linear smoothing and generalized estimation equations (GEE) is introduced to estimate the two parameter vectors as well as the unknown link Downloadable! In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on the combination of the local linear smoothing and generalized estimation equations (GEE) is introduced to estimate the two parameter vectors as well as the
One of the most common approaches to analyze longitudinal data is by assuming marginal models; see Pepe and Anderson (1994). Consider the marginal longitudinal generalized partially linear single-index model (2) E E β γ for and . Besides, is assumed to be a known monotonic and differentiable link function. Liang and Tsai (2014) considered a partially linear single-index longitudinal data model by using polynomial splines to approximate the unknown link function, but their discussion was limited to the sparse and balanced lon-gitudinal data case. In contrast, as mentioned in Section 1, our framework For partially linear single-index models, it is interesting to test whether the single-index part can be replaced by a linear expression of the index function. That means we are interested in testing (3.1) H 0 : g ( t ) = α 0 + α 1 t ↔ H 1 : g ( t ) ≠ α 0 + α 1 t , where α 0 and α 1 are two unknown parameters.