The maximum likelihood estimator is then the solution of a linearly constrained convex minimization problem. This problem turns out to be numerically difficult.
We consider the problem of estimating a density function that is assumed to be log-concave. This semi- parametric model includes many well-known parametric�...
Missing: Convex | Show results with:Convex
Maximum likelihood estimation (MLE) is an estimation method that allows us to use a sample to estimate the parameters of the probability distribution that�...
In the iterative algorithm that we propose in Section 4 for computing the maximum likelihood estimator, we need to find convex hulls and triangulations at each�...
We show that finding the log-concave maximum likelihood estimate is equivalent to solving a collection of polynomial-exponential systems of a special form.
We study nonparametric estimation of convex regression and density functions by methods of least squares (in the regression and density cases) and maximum�...
Jul 16, 2018 � I am looking to compute maximum likelihood estimators for μ and σ2, given n i.i.d random variables drawn from a Gaussian distribution. I�...
Missing: Density | Show results with:Density
Oct 10, 2023 � We study probability density functions that are log-concave. Despite the space of all such densities being infinite-dimensional, the maximum�...
It is known that the maximum likelihood estimator for the log-density is always a piecewise linear function with at most as many knots as observations, but�...
We study nonparametric maximum likelihood estimation of a log-concave probability density and its dis- tribution and hazard function.
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