An EM Algorithm for Capsule Regression

An EM-based Multi-Step Piecewise Surface Regression Learning Algorithm

A multi-step Expectation Maximization based (EM-based) algorithm is proposed to solve piecewise surface regression problem which has typical applications in market segmentation research, identification of consumer behavior patterns, weather patterns in meteorological research, and so on. The multiple steps involved are local regression on each data point of the training data set and a small set...

Regression analysis for current status data using the EM algorithm.

We propose new expectation-maximization algorithms to analyze current status data under two popular semiparametric regression models: the proportional hazards (PH) model and the proportional odds (PO) model. Monotone splines are used to model the baseline cumulative hazard function in the PH model and the baseline odds function in the PO model. The proposed algorithms are derived by exploiting ...

An Improved EM algorithm

In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we give a short proof of EM's convergence. Then, we implement experiments with the expectation maximization algorithm (We implement all the experiments on Gaussion mixture model (GMM) ). Our experiment...

An EM Algorithm for Multivariate Mixed Poisson Regression Models and its Application

Although the literature on univariate count regression models allowing for overdispersion is huge, there are few multivariate count regression models allowing for correlation and overdiseprsion. The latter models can find applications in several disciplines such as epidemiology, marketing, sports statistics, criminology, just to name a few. In this paper, we propose a general EM algorithm to fa...

An EM-algorithm for dynamic SPECT tomography