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PROXIMAL NEWTON-TYPE METHODS FOR MINIMIZING COMPOSITE FUNCTIONS
convex optimization nonsmooth optimization
2015/7/3
We generalize Newton-type methods for minimizing smooth functions to handle a
sum of two convex functions: a smooth function and a nonsmooth function with a simple proximal mapping.
Comparing composite likelihood methods based on pairs for spatial Gaussian random fieldsM
Covariance estimation Geostatistics Large datasets Tapering
2013/6/14
In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators have nice features when...
Affine Invariant Divergences associated with Composite Scores and its Applications
composite score divergence Bregman score H¨older score affine invariance
2013/6/14
In statistical analysis, measuring a score of predictive performance is an important task. In many scientific fields, appropriate scores were tailored to tackle the problems at hand. A proper score is...
Composite likelihood estimation of sparse Gaussian graphical models with symmetry
Variable selection model selection penalized estimation Gaussian graphical model concentration matrix partial correlation matrix
2012/9/17
In this article, we discuss the composite likelihood estimation of sparse Gaussian graph-ical models. When there are symmetry constraints on the concentration matrix or partial correlation matrix, the...
Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
Composite likelihood coordinatewise optimization Ising model minorization–maximization principle NP-dimension asymptotic theory HIV drug resistance database.
2012/9/17
The Ising model is a useful tool for studying complex interactions within a system. The estimation of such a model, however, is rather challenging, especially in the presence of high-dimensional param...
Minimax testing of a composite null hypothesis defined via a quadratic functional in the model of regression
Nonparametric hypotheses testing sharp asymptotics separation rates minimax approach high-dimensional regression.
2012/9/17
We consider the problem of testing a particular type of composite null hypothesis under a nonparametric multivariate regression model. For a given quadraticfunctional Q, the null hypothesis states tha...
Iteration Complexity of Randomized Block-Coordinate Descent Methods for Minimizing a Composite Function
Block coordinate descent iteration complexity composite minimization
2011/7/19
In this paper we develop a randomized block-coordinate descent method for minimizing the sum of a smooth and a simple nonsmooth block-separable convex function and prove that it obtains an $\epsilon$-...
Testing composite hypotheses, Hermite polynomials and optimal estimation of a nonsmooth functional
Best polynomial approximation ℓ 1 norm composite hypothe-ses Hermite polynomial minimax lower bound nonsmooth functional optimal rate of convergence
2011/6/17
A general lower bound is developed for the minimax risk when
estimating an arbitrary functional. The bound is based on testing
two composite hypotheses and is shown to be effective in estimating
th...
Estimating composite functions by model selection
Curve estimation model selection composite functions
2011/3/21
We consider the problem of estimating a function $s$ on $[-1,1]^{k}$ for large values of $k$ by looking for some best approximation by composite functions of the form $g\circ u$. Our solution is based...
Extended Generalized-K (EGK): A New Simple and General Model for Composite Fading Channels
Information Theory (cs.IT) Probability (math.PR) Statistics Theory (math.ST)
2010/12/17
In this paper, we introduce a generalized composite fading distribution (termed extended generalized-K (EGK)) to model the envelope and the power of the received signal in millimeter wave (60 GHz or a...
A Law of Likelihood for Composite Hypotheses
likelihood paradigm likelihood ratio profile likelihood statistical evidence support interval support set
2010/3/17
The law of likelihood underlies a general framework, known as the likelihood paradigm, for representing
and interpreting statistical evidence. As stated, the law applies only to simple hypotheses, an...