We devise a classification algorithm based on generalised linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the result...

Nonparametric additive modeling is a fundamental tool for statistical data analysis which allows flexible functional forms for conditional mean or quantile functions but avoids the curse of dimensionality for fully nonparametric methods induced by high-dimensional covariates. This paper proposes empirical likelihood-based inference methods for unknown functions in three types of nonparametric a...

objective: the main objective of this research was estimation of genetic parameters for five consecutive measurements of egg weights in isfahan fowl using multi trait model and random regression models. methods: the statistical models included generation-hatch as a fixed effect, weeks of age as a covariate and additive genetic and individual permanent environmental effects as random effects. th...

The distributed lag effect of ambient particulate air pollution that can be attributed to all cause mortality in Kathmandu valley, Nepal is estimated through generalized linear model (GLM) and generalized additive model (GAM) with autoregressive count dependent variable. Models are based upon daily time series data on mortality collected from the leading hospitals and exposure collected from th...

Severe atmospheric and road surface conditions on rural highways create a variety of transportation hazards. Variable Speed Limit (VSL) systems can mitigate the dangers caused by these conditions by displaying prudent maximum speed limits that reflect the prevailing atmospheric and road surface conditions. This paper discusses the design and implementation of a fuzzy rulebased system (FRBS) tha...

As a flexible alternative to the Cox model, the additive risk model assumes that the hazard function is the sum of the baseline hazard and a regression function of covariates. For right censored survival data when variable selection is needed along with model estimation, we propose a path consistent model selector using a modified Lasso approach, under the additive risk model assumption. We sho...