Statistical guarantees for the EM algorithm: From population to sample-based analysis [PDF]
arXiv.org, 2014 We develop a general framework for proving rigorous guarantees on the performance of the EM algorithm and a variant known as gradient EM. Our analysis is divided into two parts: a treatment of these algorithms at the population level (in the limit of ...
Sivaraman Balakrishnan +2 more
semanticscholar +1 more source
Joint lifetime modeling with matrix distributions
Dependence Modeling, 2023 Acyclic phase-type (PH) distributions have been a popular tool in survival analysis, thanks to their natural interpretation in terms of aging toward its inevitable absorption.
Albrecher Hansjörg +2 more
doaj +1 more source
Variable selection in finite mixture of median regression models using skew-normal distribution
Statistical Theory and Related Fields, 2023 A regression model with skew-normal errors provides a useful extension for traditional normal regression models when the data involve asymmetric outcomes.
Xin Zeng, Yuanyuan Ju, Liucang Wu
doaj +1 more source
Evidential-EM Algorithm Applied to Progressively Censored Observations [PDF]
, 2014 Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data.
A.P. Dempster +5 more
core +5 more sources
Efficient training algorithms for HMMs using incremental estimation [PDF]
, 1998 Typically, parameter estimation for a hidden Markov model (HMM) is performed using an expectation-maximization (EM) algorithm with the maximum-likelihood (ML) criterion. The EM algorithm is an iterative scheme that is well-defined and numerically stable,
Gotoh, Y. +2 more
core +1 more source
Performance Enhancement of Wi-Fi Fingerprinting-Based IPS by Accurate Parameter Estimation of Censored and Dropped Data [PDF]
Radioengineering, 2019 In complex indoor environments, the censoring, dropping, and multi-component problems may present in the observable data. This is due to the attenuation of signals, the unexpected operation of equipments, and the changing surrounding environment ...
T. K. Vu, M. K. Hoang, H. L. Le
doaj
On the Estimation of Nonrandom Signal Coefficients from Jittered Samples [PDF]
, 2010 This paper examines the problem of estimating the parameters of a bandlimited signal from samples corrupted by random jitter (timing noise) and additive iid Gaussian noise, where the signal lies in the span of a finite basis.
Goyal, Vivek K, Weller, Daniel S.
core +5 more sources
CHIME: Clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality
Annals of Statistics, 2019 Unsupervised learning is an important problem in statistics and machine learning with a wide range of applications. In this paper, we study clustering of high-dimensional Gaussian mixtures and propose a procedure, called CHIME, that is based on the EM ...
T. Cai, Jing Ma, Linjun Zhang
semanticscholar +1 more source
Spatially multi-scale dynamic factor modeling via sparse estimation
International Journal of Mathematics for Industry, 2019 In many spatio-temporal data, their spatial variations have inherent global and local structures. The spatially continuous dynamic factor model (SCDFM) decomposes the spatio-temporal data into a small number of spatial and temporal variations, where the ...
Takamitsu Araki, Shotaro Akaho
doaj +1 more source
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
, 2011 We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution.
Kameya, Y., Sato, T.
core +2 more sources