Name
Abstract | ||
|---|---|---|
Hidden semi-Markov models are a generalization of the well-known hidden Markov model. They allow for a greater flexibility of sojourn time distributions, which implicitly follow a geometric distribution in the case of a hidden Markov chain. The aim of this paper is to describe hsmm, a new software package for the statistical computing environment R. This package allows for the simulation and maximum likelihood estimation of hidden semi-Markov models. The implemented Expectation Maximization algorithm assumes that the time spent in the last visited state is subject to right-censoring. It is therefore not subject to the common limitation that the last visited state terminates at the last observation. Additionally, hsmm permits the user to make inferences about the underlying state sequence via the Viterbi algorithm and smoothing probabilities. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.csda.2008.08.025 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
r package,sojourn time distribution,expectation maximization algorithm,new software package,viterbi algorithm,hidden semi-markov model,state terminates,last observation,hidden markov chain,well-known hidden markov model,underlying state sequence,right censoring,hidden semi markov model,maximum likelihood estimate,statistical computing,geometric distribution,hidden markov model | Econometrics,Forward algorithm,Expectation–maximization algorithm,Markov model,Markov chain,Computational statistics,Statistics,Hidden Markov model,Mathematics,Viterbi algorithm,Hidden semi-Markov model | Journal |
Volume | Issue | ISSN |
54 | 3 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
10 | 0.97 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Bulla | 1 | 39 | 3.15 |
| Ingo Bulla | 2 | 52 | 5.15 |
| Oleg Nenadić | 3 | 10 | 0.97 |