Unimodal density estimation using Bernstein polynomials
نویسندگان
چکیده
منابع مشابه
Unimodal density estimation using Bernstein polynomials
The estimation of probability density functions is one of the fundamental aspects of any statistical inference. Many data analyses are based on an assumed family of parametric models, which are known to be unimodal (e.g., exponential family, etc.). Often a histogram suggests the unimodality of the underlying density function. Parametric assumptions, however, may not be adequate for many inferen...
متن کاملConvergence Rates for Density Estimation with Bernstein Polynomials
Mixture models for density estimation provide a very useful set up for the Bayesian or the maximum likelihood approach. For a density on the unit interval, mixtures of beta densities form a flexible model. The class of Bernstein densities is a much smaller subclass of the beta mixtures defined by Bernstein polynomials, which can approximate any continuous density. A Bernstein polynomial prior i...
متن کاملUnimodal Kernel Density Estimation by Data Sharpening
We discuss a robust data sharpening method for rendering a standard kernel estimator, with a given bandwidth, unimodal. It has theoretical and numerical properties of the type that one would like such a technique to enjoy. In particular, we show theoretically that, with probability converging to 1 as sample size diverges, our technique alters the kernel estimator only in places where the latter...
متن کاملInformation-theoretic approach to unimodal density estimation
Ex = (I + y / t) (l +. r y / : ') El0 = (1 + ! / / Z) (l + .r2y/:3) all R-multiples of Es. The error positions in this case are (j. 1. l) , In general, when decoding an error relative to an algebraic geometric code C*(D. 7rtP), there is a vector space S (g) l of error-locator functions of dimension Z(m)-e. Most algorithms settle for any element of this space as an error-locator and deal with ex...
متن کاملRegression estimation based on Bernstein density copulas∗
The regression function can be expressed in term of copula density and marginal distributions. In this paper, we propose a new method of estimating a regression function using the Bernstein estimator for the copula density and the empirical distributions for the marginal distributions. The method is fully non-parametric and easy to implement. We provide some asymptotic results related to this c...
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: Computational Statistics & Data Analysis
سال: 2014
ISSN: 0167-9473
DOI: 10.1016/j.csda.2013.10.021