Nonparametric maximum likelihood approach to multiple change-point problems
نویسندگان
چکیده
منابع مشابه
A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation
We propose a class of penalized nonparametric maximum likelihood estimators (NPMLEs) for the species richness problem. We use a penalty term on the likelihood because likelihood estimators that lack it have an extreme instability problem. The estimators are constructed using a conditional likelihood that is simpler than the full likelihood. We show that the full-likelihood NPMLE solution given ...
متن کاملNonparametric Stochastic Frontiers: A Local Maximum Likelihood Approach
This paper proposes a nonparametric approach for stochastic frontier (SF) models based on local maximum likelihood techniques. The SF model is presented as encompassing some anchorage parametric model in a nonparametric way. First, we derive asymptotic properties of the estimator for the general case (local linear approximations). Then the results are tailored to a SF model where the convoluted...
متن کاملNonparametric Density Estimation: A Piecewise Maximum Likelihood Approach
The effort of recovering the features (such as the number of modes and overall shape) of unknown densities leads to nonparametric curve estimation under order restrictions. We introduce a flexible class of nonparametric densities called a-regular shaped densities, and propose the Piecewise Maximum Likelihood Estimate (PMLE) which has an attractive "automatic" bandwidth selection feature. Spline...
متن کاملAutomatic Generalized Nonparametric Regression via Maximum Likelihood
A relatively recent development in nonparametric regression is the representation of spline-based smoothers as mixed model fits. In particular, generalized nonparametric regression (e.g. smoothingwith a binary response) corresponds to fitting a generalized linear mixedmodel. Automation, or data-driven smoothing parameter selection, can be achieved via (restricted) maximum likelihood estimation ...
متن کاملBayesian Hierarchical Nonparametric Inference for Change-point Problems
SUMMARY Bayesian nonparametric inference for a nonsequential change-point problem is studied. We use a mixture of products of Dirichlet processes as our prior distribution. This allows the data before and after the change-point to be dependent, even when the change point is known. A Gibbs sampler algorithm is also proposed in order to overcome analytic diiculties in computing the posterior dist...
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: The Annals of Statistics
سال: 2014
ISSN: 0090-5364
DOI: 10.1214/14-aos1210