Approximate maximum likelihood estimation for population genetic inference

Approximate maximum likelihood estimation for population genetic inference.

In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. However, these methods such as Approximate Bayesian Computation (ABC) can be inefficient in high-dimensional problems. This led to the development...

Approximate Maximum Likelihood Method for Frequency Estimation

A frequency can be estimated by few Discrete Fourier Transform (DFT) coefficients, see Rife and Vincent (1970), Quinn (1994, 1997). This approach is computationally efficient. However, the statistical efficiency of the estimator depends on the location of the frequency. In this paper, we explain this approach from a point of view of an Approximate Maximum Likelihood (AML) method. Then we enhanc...

Approximate maximum-likelihood estimation using semidefinite programming

We consider semidefinite relaxations of a quadratic optimization problem with polynomial constraints. This is an extension of quadratic problems with boolean variables. Such combinatorial problems can in general not be solved in polynomial time. Semidefinite relaxations has been proposed as a promising technique to give provable good bounds on certain boolean quadratic problems in polynomial ti...

Approximate Maximum Likelihood Estimation in Linear Regression*

A b s t r a c t. The application of the ML method in linear regression requires a parametric form for the error density. When this is not available, the density may be parameterized by its cumulants (~i) and the ML then applied. Results , (i+2)/2 are obtained when the standardized cumulants (~/~) satisfy ~/~ = ~i+2/~2 = O(v i) as v-~ 0 for i > 0.

Local Maximum Likelihood Estimation and Inference