Process Modeling by Bayesian Latent Variable Regression Mohamed N. Nounou, Bhavik R. Bakshi Prem K. Goel, Xiaotong Shen Department of Chemical Engineering Department of Statistics The Ohio State University, Columbus, OH 43210, USA Abstract Large quantities of measured data are being routinely collected in a variety of industries and used for extracting linear models for tasks such as, process c...

BACKGROUND A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a cer...

It is often assumed that the participants of a conversation try to avoid simultaneous starts or lengthy silences. For this reason, they may tend to synchronize rhythmically with each other’s speech. A model of conversational turn-taking based on the idea of coupled oscillators has been suggested by Wilson & Wilson [1]. However, the model has received only weak empirical support from previous st...

A catchment area (CA) is the geographic area and population from which a cancer center draws patients. Defining a CA allows a cancer center to describe its primary patient population and assess how well it meets the needs of cancer patients within the CA. A CA definition is required for cancer centers applying for National Cancer Institute (NCI)-designated cancer center status. In this research...

A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observation...

We present self-modeling regression models for flexible nonparametric modeling of multiple outcomes measured longitudinally. Based on penalized regression splines, the models borrow strength across multiple outcomes by specifying a global time profile, thereby yielding a means of dimension reduction and estimates of trend more precise than those based on univariate regressions. The proposed mod...

This article describes a method for predicting human motion where some part of the body, such as the pelvis or foot does not move. The posture at any given time can be approximated using a linkage of articulated segments. The angles between the segments describe the posture. During the reach, these angles will vary describing a function that varies over time. Data may be collected on individual...

A new algorithm for modeling regression curve is put forward in the paper, it combines the B-spline network with improved support vector regression. Our experimental results on simulated data demonstrate that it is feasible and effective.

Semicompeting risks data are often encountered in clinical trials with intermediate endpoints subject to dependent censoring from informative dropout. Unlike with competing risks data, dropout may not be dependently censored by the intermediate event. There has recently been increased attention to these data, in particular inferences about the marginal distribution of the intermediate event wit...

Functional data often exhibit a common shape but also variations in amplitude and phase across curves. The analysis often proceed by synchronization of the data through curve registration. In this paper we propose a Bayesian Hierarchical model for curve registration. Our hierarchical model provides a formal account of amplitude and phase variability while borrowing strength from the data across...