Add to ORKGA Logspline method of estimating an unknown density function f based on sample data is studied. Our approach is to use maximum likelihood estimation to estimate the unknown density function from a space of linear splines that have a finite number of fixed uniform knots. In the end of this thesis, the method is applied to a real survival data set of lung cancer patients
In Statistics, log-concave density estimation is a central problem within the field of nonparametric...
Publisher Copyright: © 2022 The Author(s)We study probability density functions that are log-concave...
Probability density functions are estimated by the method of maximum likelihood in sequences of regu...
Logspline density estimation is developed for data that may be right censored, left censored or inte...
AbstractWe consider bivariate logspline density estimation for tomography data. In the usual logspli...
We consider bivariate logspline density estimation for tomography data. In the usual logspline densi...
Most recent maximum likelihood approaches to independent component analysis (ICA) are based on nonpa...
Free knot spline functions are used to estimate the underlying density function of a random sample. ...
Given a random sample from a continuous and positive density f , the logistic transformation is app...
Density estimation plays a fundamental role in many areas including statistics and machine learning....
In recent years, log-concave density estimation via maximum likelihood estimation has emerged as a f...
Maximum likelihood estimation of a log-concave density has attracted considerable attention over the...
Maximum likelihood estimation of a log-concave density has attracted considerable attention over the...
Density estimation is a fundamental statistical problem. Many methods are eithersensitive to model m...
Abstract: Maximum likelihood predictive densities (MLPD) for a future lognormal obser-vation are obt...
In Statistics, log-concave density estimation is a central problem within the field of nonparametric...
Publisher Copyright: © 2022 The Author(s)We study probability density functions that are log-concave...
Probability density functions are estimated by the method of maximum likelihood in sequences of regu...
Logspline density estimation is developed for data that may be right censored, left censored or inte...
AbstractWe consider bivariate logspline density estimation for tomography data. In the usual logspli...
We consider bivariate logspline density estimation for tomography data. In the usual logspline densi...
Most recent maximum likelihood approaches to independent component analysis (ICA) are based on nonpa...
Free knot spline functions are used to estimate the underlying density function of a random sample. ...
Given a random sample from a continuous and positive density f , the logistic transformation is app...
Density estimation plays a fundamental role in many areas including statistics and machine learning....
In recent years, log-concave density estimation via maximum likelihood estimation has emerged as a f...
Maximum likelihood estimation of a log-concave density has attracted considerable attention over the...
Maximum likelihood estimation of a log-concave density has attracted considerable attention over the...
Density estimation is a fundamental statistical problem. Many methods are eithersensitive to model m...
Abstract: Maximum likelihood predictive densities (MLPD) for a future lognormal obser-vation are obt...
In Statistics, log-concave density estimation is a central problem within the field of nonparametric...
Publisher Copyright: © 2022 The Author(s)We study probability density functions that are log-concave...
Probability density functions are estimated by the method of maximum likelihood in sequences of regu...