Traffic flow data, in the form of multiple time series of aggregated traffic volume observed
from various vehicle detector stations, are investigated as a motivating example. By the very …

Rapid development for modeling big data requires effective and efficient methods for
estimating the parameters involved. Although several accelerated Expectation-Maximization …

Fast incremental expectation maximization (FIEM) is a version of the EM framework for large
datasets. In this paper, we first recast FIEM and other incremental EM type algorithms in the …

This research is a case study of an information technology (IT) solution company. There is a
problem that is quite crucial in the hardware sales strategy which makes it difficult for the …

Data augmentation (DA) algorithms are slow in massive data settings due to multiple passes
through the entire data. We address this problem by develo** a DA extension that exploits …

Abstract The Expectation Maximization (EM) algorithm is widely used in latent variable
model inference. However, when data are distributed across various locations, directly …

Finite mixture models are powerful tools for modeling and analyzing heterogeneous data.
Parameter estimation is typically carried out using maximum likelihood estimation via the …

Classification and clustering methods based on univariate functions have been well
developed. Recent work has extended the techniques to the domain of bivariate functions by …

In contrast to the popular Cox model which presents a multiplicative covariate effect
specification on the time to event hazards, the semiparametric additive risks model (ARM) …

Abstract Fast Incremental Expectation Maximization was introduced to design Expectation-
Maximization (EM) for the large scale learning framework involving finite-sum and possibly …