Deinterleaving Finite Memory Processes Via Penalized Maximum Likelihood
نویسندگان
چکیده
منابع مشابه
Maximum likelihood, profile likelihood, and penalized likelihood: a primer.
The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive little or no education in the conceptual underpinnings of the approach. Here we provide a primer on maximum likelihood and some important extensions which have proven useful in epidemiologic research, and which reveal connections between maximum likelihood and Bayesian methods. For a given data set...
متن کاملVariable Selection via Penalized Likelihood
Variable selection is vital to statistical data analyses. Many of procedures in use are ad hoc stepwise selection procedures, which are computationally expensive and ignore stochastic errors in the variable selection process of previous steps. An automatic and simultaneous variable selection procedure can be obtained by using a penalized likelihood method. In traditional linear models, the best...
متن کاملPenalized maximum likelihood for multivariate Gaussian mixture
In this paper, we first consider the parameter estimation of a multivariate random process distribution using multivariate Gaussian mixture law. The labels of the mixture are allowed to have a general probability law which gives the possibility to modelize a temporal structure of the process under study. We generalize the case of univariate Gaussian mixture in [1] to show that the likelihood is...
متن کاملQuasi-maximum Likelihood Estimation of Long-memory Limiting Aggregate Processes
We consider the application of the limiting aggregate model derived by Tsai and Chan (2005d) for modeling aggregated long-memory data. The model is characterized by the fractional integration order of the original process and may be useful for (i) modeling discrete-time data with sufficiently long sampling intervals, for example, annual data, and/or (ii) studying the fractional integration orde...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2012
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2012.2211333