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- W3217677003 abstract "Sufficient conditions are provided under which the log-likelihood ratio test statistic fails to have a limiting chi-squared distribution under the null hypothesis when testing between one and two components under a general two-component mixture model, but rather tends to infinity in probability. These conditions are verified when the component densities describe continuous-time, discrete-statespace Markov chains and the results are illustrated via a parametric bootstrap simulation on an analysis of the migrations over time of a set of corporate bonds ratings. The precise limiting distribution is derived in a simple case with two states, one of which is absorbing which leads to a right-censored exponential scale mixture model. In that case, when centred by a function growing logarithmically in the sample size, the statistic has a limiting distribution of Gumbel extreme-value type rather than chi-squared." @default.
- W3217677003 created "2021-12-06" @default.
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- W3217677003 date "2021-11-01" @default.
- W3217677003 modified "2023-10-17" @default.
- W3217677003 title "Asymptotics for Markov chain mixture detection" @default.
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- W3217677003 doi "https://doi.org/10.1016/j.ecosta.2021.11.004" @default.
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