Browsing by Author "Bulla, Jan"

The parameters of a hidden Markov model (HMM) can be estimated by numerical maximization of the log-likelihood function or, more popularly, using the expectation-maximization (EM) algorithm. In its standard implementation the latter is unsuitable for fitting stationary hidden Markov models (HMMs). We show how it can be modified to achieve this. We propose a hybrid algorithm that is designed to combine the advantageous features of the two algorithms and compare the performance of the three algorithms using simulated data from a designed experiment, and a real data set. The properties investigated are speed of convergence, stability, dependence on initial values, different parameterizations. We also describe the results of an experiment to assess the true coverage probability of bootstrap-based confidence intervals for the parameters.

Background: Methods of determining whether or not any particular HIV-1 sequence stems - completely or in part from some unknown HIV-1 subtype are important for the design of vaccines and molecular detection systems, as well as for epidemiological monitoring. Nevertheless, a single algorithm only, the Branching Index (BI), has been developed for this task so far. Moving along the genome of a query sequence in a sliding window, the BI computes a ratio quantifying how closely the query sequence clusters with a subtype clade. In its current version, however, the BI does not provide predicted boundaries of unknown fragments. Results: We have developed Unknown Subtype Finder (USF), an algorithm based on a probabilistic model, which automatically determines which parts of an input sequence originate from a subtype yet unknown. The underlying model is based on a simple profile hidden Markov model (pHMM) for each known subtype and an additional pHMM for an unknown subtype. The emission probabilities of the latter are estimated using the emission frequencies of the known subtypes by means of a (position-wise) probabilistic model for the emergence of new subtypes. We have applied USF to SIV and HIV-1 sequences formerly classified as having emerged from an unknown subtype. Moreover, we have evaluated its performance on artificial HIV-1 recombinants and nonrecombinant HIV-1 sequences. The results have been compared with the corresponding results of the BI. Conclusions: Our results demonstrate that USF is suitable for detecting segments in HIV-1 sequences stemming from yet unknown subtypes. Comparing USF with the BI shows that our algorithm performs as good as the BI or better.

Endothelin (ETB)-receptors mediate anti-apoptotic actions. Lack of functional ETB-receptors leads to increased neuronal apoptosis in the hippocampus. The increased apoptosis must be compensated by other mechanisms, however, as ETB-deficient rats display normal overall brain morphology. To illuminate on brain plasticity in ETB-receptor deficiency, we studied the expression and function of another neuroprotective system, the cannabinoid CB1-receptors, in ETB-deficient hippocampus. We show that CB1 expression in hippocampus increases postnatally in all rats but that the increase in CB1-receptor expression is significantly higher in ETB-deficient compared to wildtype littermates. Neuronal apoptosis decreases during brain maturation but remains on a significantly higher level in the ETB-deficient compared to wildtype dentate. When investigating survival of hippocampal neurons in culture, we found significant protection against hypoxia-induced cell death with CB1-analogs (noladin, (9-tetrahydrocannabinol) only in ETB-deficient neurons. We suggest that CB1-receptor upregulation in the ETB-mutant hippocampus reflects an attempt to compensate for the lack of ETB-receptors.

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. (C) 2008 Elsevier B.V. All rights reserved.

Hidden Markov models reproduce most of the stylized facts about daily series of returns. A notable exception is the inability of the models to reproduce one ubiquitous feature of such time series, namely the slow decay in the autocorrelation function of the squared returns. It is shown that this stylized fact can be described much better by means of hidden semi-Markov models. This is illustrated by examining the fit of two such models to 18 series of daily sector returns. (c) 2006 Elsevier B.V. All rights reserved.

This paper investigates the time-varying behavior of systematic risk for 18 pan-European sectors. Using weekly data over the period 1987-2005, six different modeling techniques in addition to the standard constant coefficient model are employed: a bivariate t-GARCH(1,1) model, two Kalman filter (KF)-based approaches, a bivariate stochastic volatility model estimated via the efficient Monte Carlo likelihood technique as well as two Markov switching models. A comparison of ex-ante forecast performances of the different models indicate that the random walk process in connection with the KF is the preferred model to describe and forecast the time-varying behavior of sector betas in a European context.