In this paper we introduce and develop robust versions of quasilikelihood functions for model selection via an analysis-of-deviance type of procedure in generalized linear models and longitudinal data analysis. These robust functions are built upon natural classes of robust estimators and can be seen as weighted versions of their classical counterparts. The asymptotic theory of these test stati...

Generalized linear models are one of the most widely used tools of the data analyst. However, the model assumes that the structure of the regression relationship between the response and the covariates is linear on a known transformed scale. We focus here on diierent methods to perform the same type of analyses. These involve using nonparametric models to determine the relationship between the ...

The typical generalized linear model for a regression of a response Y on predictors (X;Z) has conditional mean function based upon a linear combination of (X;Z). We generalize these models to have a nonparametric component, replacing the linear combination T 0 X + T 0 Z by 0( T 0 X) + T 0 Z, where 0( ) is an unknown function. We call these generalized partially linear single-index models (GPLSI...

Christoffersen, Jacobs, and Ornthanalai (2012) (CJO) propose an interesting and useful class of generalized autoregressive conditional heteroskedasticity (GARCH)-like models with dynamic jump intensity, and find evidence that the models not only fit returns data better than some commonly used benchmarks but also provide substantial improvements in option pricing performance. While such models p...

This note describes ANOVAs and how they can be implemented in SPM. For each type of ANOVA we show what are the relevant statistical models and how they can be implemented in a GLM. We give examples of how main effects and interactions can be tested for using F-contrasts in SPM. We also show how to implement a repeated-measures M-way ANOVA with partitioned errors using a two-level procedure in SPM.

Differential equations have been used in statistics to define functions such as probability densities. But the idea of using differential equation formulations of stochastic models has a much wider scope. The author gives several examples, including simultaneous estimation of a regression model and residual density, monotone smoothing, specification of a link function, differential equation mod...

Current opinion regarding the selection of link function in binary response models is that the probit and logit links give essentially similar results. This seems to be true for univariate binary response models; however, for multivariate binary response models such advice is misleading. We address a gap in the literature by empirically examining the relationship between link function selection...

The core inverse of matrix is generalized inverse which is in some sense in-between the group and Moore–Penrose inverse. In this paper a generalization of core inverse and core partial order to Hilbert space operator case is presented. Some properties are generalized and some new ones are proved. Connections with other generalized inverses are obtained. The useful matrix representations of oper...

The paper introduces a new generalized closed set via semi local function. Its relationship with other existing generalized closed sets are established. It’s basic properties are discussed. A new decomposition of continuity is derived. General Terms Ideal topological space, local function and semi-local function.