Additive models in high dimensions

Target setting in additive models with preferences and interval data

High Dimensional Bayesian Optimisation and Bandits via Additive Models

Bayesian Optimisation (BO) is a technique used in optimising a D-dimensional function which is typically expensive to evaluate. While there have been many successes for BO in low dimensions, scaling it to high dimensions has been notoriously difficult. Existing literature on the topic are under very restrictive settings. In this paper, we identify two key challenges in this endeavour. We tackle...

Estimation of Variance Components for Body Weight of Moghani Sheep Using B-Spline Random Regression Models

The aim of the present study was the estimation of (co) variance components and genetic parameters for body weight of Moghani sheep, using random regression models based on B-Splines functions. The data set included 9165 body weight records from 60 to 360 days of age from 2811 Moghani sheep, collected between 1994 to 2013 from Jafar-Abad Animal Research and Breeding Institute, Ardabil province,...

Bayesian Irt Models Incorporating General and Specific Abilities

IRT-based models with a general ability and several specific ability dimensions are useful. Studies have looked at item response models where the general and specific ability dimensions form a hierarchical structure. It is also possible that the general and specific abilities directly underlie all test items. A multidimensional IRT model with such an additive structure is proposed under the Bay...

Semiparametric regression models with additive nonparametric components and high dimensional parametric components

This paper concerns semiparametric regression models with additive nonparametric components and high dimensional parametric components under sparsity assumptions. To achieve simultaneous model selection for both nonparametric and parametric parts, we introduce a penalty that combines the adaptive empirical L2-norms of the nonparametric component functions and the SCAD penalty on the coefficient...

Exploring the Use of Random Regression Models withLegendre Polynomials to Analyze Clutch Sizein Iranian Native Fowl

Random regression models (RRM) have become common for the analysis of longitudinal data or repeated records on individual over time. The goal of this paper was to explore the use of random regression models with orthogonal / Legendre polynomials (RRL) to analyze new repeated measures called clutch size (CS) as a meristic trait for Iranian native fowl. Legendre polynomial functions of increasing...