Add to ORKGGeneralized varying coefficient models (GVCMs) form a family of statistical utilities that are applicable to real world questions for exploring associations between covariates and response variables. Researchers frequently fit GVCMs with particular link transformation functions. It is vital to recognize that to invest a model with a wrong link could provide extremely misleading knowledge. This thesis intends to bypass the actual form of the link function and explore a set of GVCMs whose link functions are monotonic. With the monotonicity being secured, this thesis endeavours to make use of the maximum rank correlation idea and proposes a maximum rank correlation estimation (MRCE) method for GVCMs. In addition to the introduction of MRCE, th...
A new approach is proposed to estimate a large class of multivariate volatility models. The method ...
The method of generalized estimating equations (GEEs) has been criticized recently for a failure to ...
Exceptional Model Mining strives to find coherent subgroups of the dataset where multiple target att...
Generalized varying coefficient models (GVCMs) form a family of statistical utilities that are appli...
We propose a new estimator, called the Generalized Maximum Rank Correlation Estimator (GMRC), of the...
The paper considers estimation of a model.b; = D F ( x//3,, u,), where the composite transforma-ti...
This paper presents a nonparametric and distribution-free estimator for the function h*, of observab...
The maximum association between two multivariate variables X and Y is defined as the maximal value t...
The maximum association between two multivariate variables X and Y is defined as the maximal value t...
Han’s maximum rank correlation (MRC) estimator is shown to be√ n-consistent and asymptotically norma...
Wepropose a new estimationmethod for generalized varying coefficient models where the link function ...
This paper proposes a class of parametric correlation models that apply a two-layer autoregressive-m...
Reduced-rank restrictions can add useful parsimony to coefficient matrices of multivariate models, b...
In this paper, we study the model selection and structure specification for the generalised semi-var...
Summary We consider a generalized regression model with a partially linear index. The index contains...
A new approach is proposed to estimate a large class of multivariate volatility models. The method ...
The method of generalized estimating equations (GEEs) has been criticized recently for a failure to ...
Exceptional Model Mining strives to find coherent subgroups of the dataset where multiple target att...
Generalized varying coefficient models (GVCMs) form a family of statistical utilities that are appli...
We propose a new estimator, called the Generalized Maximum Rank Correlation Estimator (GMRC), of the...
The paper considers estimation of a model.b; = D F ( x//3,, u,), where the composite transforma-ti...
This paper presents a nonparametric and distribution-free estimator for the function h*, of observab...
The maximum association between two multivariate variables X and Y is defined as the maximal value t...
The maximum association between two multivariate variables X and Y is defined as the maximal value t...
Han’s maximum rank correlation (MRC) estimator is shown to be√ n-consistent and asymptotically norma...
Wepropose a new estimationmethod for generalized varying coefficient models where the link function ...
This paper proposes a class of parametric correlation models that apply a two-layer autoregressive-m...
Reduced-rank restrictions can add useful parsimony to coefficient matrices of multivariate models, b...
In this paper, we study the model selection and structure specification for the generalised semi-var...
Summary We consider a generalized regression model with a partially linear index. The index contains...
A new approach is proposed to estimate a large class of multivariate volatility models. The method ...
The method of generalized estimating equations (GEEs) has been criticized recently for a failure to ...
Exceptional Model Mining strives to find coherent subgroups of the dataset where multiple target att...