38,087 research outputs found
Computing all roots of the likelihood equations of seemingly unrelated regressions
Seemingly unrelated regressions are statistical regression models based on
the Gaussian distribution. They are popular in econometrics but also arise in
graphical modeling of multivariate dependencies. In maximum likelihood
estimation, the parameters of the model are estimated by maximizing the
likelihood function, which maps the parameters to the likelihood of observing
the given data. By transforming this optimization problem into a polynomial
optimization problem, it was recently shown that the likelihood function of a
simple bivariate seemingly unrelated regressions model may have several
stationary points. Thus local maxima may complicate maximum likelihood
estimation. In this paper, we study several more complicated seemingly
unrelated regression models, and show how all stationary points of the
likelihood function can be computed using algebraic geometry.Comment: To appear in the Journal of Symbolic Computation, special issue on
Computational Algebraic Statistics. 11 page
Maximum Likelihood for Matrices with Rank Constraints
Maximum likelihood estimation is a fundamental optimization problem in
statistics. We study this problem on manifolds of matrices with bounded rank.
These represent mixtures of distributions of two independent discrete random
variables. We determine the maximum likelihood degree for a range of
determinantal varieties, and we apply numerical algebraic geometry to compute
all critical points of their likelihood functions. This led to the discovery of
maximum likelihood duality between matrices of complementary ranks, a result
proved subsequently by Draisma and Rodriguez.Comment: 22 pages, 1 figur
Maximum likelihood geometry in the presence of data zeros
Given a statistical model, the maximum likelihood degree is the number of
complex solutions to the likelihood equations for generic data. We consider
discrete algebraic statistical models and study the solutions to the likelihood
equations when the data contain zeros and are no longer generic. Focusing on
sampling and model zeros, we show that, in these cases, the solutions to the
likelihood equations are contained in a previously studied variety, the
likelihood correspondence. The number of these solutions give a lower bound on
the ML degree, and the problem of finding critical points to the likelihood
function can be partitioned into smaller and computationally easier problems
involving sampling and model zeros. We use this technique to compute a lower
bound on the ML degree for tensors of border
rank and tables of rank for ,
the first four values of for which the ML degree was previously unknown
Numerical algebraic geometry for model selection and its application to the life sciences
Researchers working with mathematical models are often confronted by the
related problems of parameter estimation, model validation, and model
selection. These are all optimization problems, well-known to be challenging
due to non-linearity, non-convexity and multiple local optima. Furthermore, the
challenges are compounded when only partial data is available. Here, we
consider polynomial models (e.g., mass-action chemical reaction networks at
steady state) and describe a framework for their analysis based on optimization
using numerical algebraic geometry. Specifically, we use probability-one
polynomial homotopy continuation methods to compute all critical points of the
objective function, then filter to recover the global optima. Our approach
exploits the geometric structures relating models and data, and we demonstrate
its utility on examples from cell signaling, synthetic biology, and
epidemiology.Comment: References added, additional clarification
Solving the 100 Swiss Francs Problem
Sturmfels offered 100 Swiss Francs in 2005 to a conjecture, which deals with
a special case of the maximum likelihood estimation for a latent class model.
This paper confirms the conjecture positively
Data-Discriminants of Likelihood Equations
Maximum likelihood estimation (MLE) is a fundamental computational problem in
statistics. The problem is to maximize the likelihood function with respect to
given data on a statistical model. An algebraic approach to this problem is to
solve a very structured parameterized polynomial system called likelihood
equations. For general choices of data, the number of complex solutions to the
likelihood equations is finite and called the ML-degree of the model. The only
solutions to the likelihood equations that are statistically meaningful are the
real/positive solutions. However, the number of real/positive solutions is not
characterized by the ML-degree. We use discriminants to classify data according
to the number of real/positive solutions of the likelihood equations. We call
these discriminants data-discriminants (DD). We develop a probabilistic
algorithm for computing DDs. Experimental results show that, for the benchmarks
we have tried, the probabilistic algorithm is more efficient than the standard
elimination algorithm. Based on the computational results, we discuss the real
root classification problem for the 3 by 3 symmetric matrix~model.Comment: 2 table
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