mvecdf

Multivariate empirical cumulative distribution evaluated at sample data

Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>. For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data.

Zhong Guan

mable

Maximum Approximate Bernstein/Beta Likelihood Estimation

mvecdf function

<dl><dt>x</dt>
<dd>an <code>n x d</code> matrix of data values, 
rows are <code>n</code> observations of \(X=(X_1,...,X_d)\)</dd></dl>

Arguments

Multivariate empirical cumulative distribution evaluated at sample data — mvecdf

<dl>

<dt>x</dt>
<dd>an <code>n x d</code> matrix of data values, 
rows are <code>n</code> observations of \(X=(X_1,...,X_d)\)</dd>

</dl>

mvecdf: Multivariate empirical cumulative distribution evaluated at sample data

Description

Usage

Value

Arguments