Over the last few years many methods have been developed for analyzing functional data with different objectives. The purpose of this paper is to predict a binary response variable in terms of a functional variable whose sample information is given by a set of curves measured without error. In order to solve this problem we formulate a functional logistic regression model and propose its estima...

BACKGROUND It has been shown that if genetic relationships among individuals are not taken into account for genome wide association studies, this may lead to false positives. To address this problem, we used Genome-wide Rapid Association using Mixed Model and Regression and principal component stratification analyses. To account for linkage disequilibrium among the significant markers, principa...

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] com...

Forty young adults and 40 older adults performed seven visuospatial information processing tasks. Factor analyses of the response times (RTs) yielded a single principal component with a similar composition in both age samples. For both samples, regressing the mean RTs of fast and slow subgroups for the seven tasks (18 conditions) on the corresponding mean RTs for their age group accounted for 9...

A variety of laboratory techniques are available to determine the expression levels of particular mRNAs present in biological samples. Depending on the laboratory resources available, the level of accuracy desired, and the number of targets to be measured, the researcher typically chooses between techniques such as quantitative PCR (Q-PCR), Northern blots, RNase protection analysis, or expressi...

We propose a principal components regression method based on maximizing joint pseudo-likelihood for responses and predictors. Our uses both predictors to select linear combinations of the relevant regression, thereby addressing an oft-cited deficiency conventional regression. The proposed estimator is shown be consistent in wide range settings, including ones with non-normal dependent observati...

The authors consider dimensionality reduction methods used for prediction, such as reduced rank regression, principal component regression and partial least squares. They show how it is possible to obtain intermediate solutions by estimating simultaneously the latent variables for the predictors and for the responses. They obtain a continuum of solutions that goes from reduced rank regression t...