Add to ORKGNew techniques of local sensitivity analysis in nonsmooth optimization are applied to the problem of determining the asymptotic distribution (generally non-normal) for solutions in stochastic optimization, and generalized M-estimation -- a reformulation of the traditional maximum likelihood problem that allows the introduction of hard constraints
The paper deals with a statistical approach to stability analysis in nonlinear stochastic programmin...
Keywords: Bayesian asymptotics Asymptotic normality Local asymptotic normality Locally asymptotic qu...
Most of statistical procedures consist in estimating parameters by minimizing (or maximizing) some c...
This paper supplements the results of a new statistical approach to the problem of incomplete inform...
Under incomplete information about the parameters of the true distribution of the random coefficient...
Stochastic approximation is one of the oldest approaches for solving stochastic optimization problem...
This thesis is primarily concerned with the investigation of asymptotic properties of the maximum l...
We study a class of random sampling-based algorithms for solving general non-convex, nondifferentiab...
AbstractIn this note some problems of asymptotic inference in a class of non-stationary stochastic p...
In certain cases statistical methods based on standard maximum likelihood asymptotics become valid a...
AbstractAsymptotically maximum likelihood estimators and estimators asymptotically minimizing criter...
Sample average approximation (SAA) is one of the most popular methods for solving stochastic optimiz...
Fortet-Mourier (FM) probability metrics are important probability metrics, which have been widely ad...
AbstractThe asymptotic distribution of multivariate M-estimates is studied. It is shown that, in gen...
We consider some asymptotic distribution theory for M-estimators of the parameters of a linear model...
The paper deals with a statistical approach to stability analysis in nonlinear stochastic programmin...
Keywords: Bayesian asymptotics Asymptotic normality Local asymptotic normality Locally asymptotic qu...
Most of statistical procedures consist in estimating parameters by minimizing (or maximizing) some c...
This paper supplements the results of a new statistical approach to the problem of incomplete inform...
Under incomplete information about the parameters of the true distribution of the random coefficient...
Stochastic approximation is one of the oldest approaches for solving stochastic optimization problem...
This thesis is primarily concerned with the investigation of asymptotic properties of the maximum l...
We study a class of random sampling-based algorithms for solving general non-convex, nondifferentiab...
AbstractIn this note some problems of asymptotic inference in a class of non-stationary stochastic p...
In certain cases statistical methods based on standard maximum likelihood asymptotics become valid a...
AbstractAsymptotically maximum likelihood estimators and estimators asymptotically minimizing criter...
Sample average approximation (SAA) is one of the most popular methods for solving stochastic optimiz...
Fortet-Mourier (FM) probability metrics are important probability metrics, which have been widely ad...
AbstractThe asymptotic distribution of multivariate M-estimates is studied. It is shown that, in gen...
We consider some asymptotic distribution theory for M-estimators of the parameters of a linear model...
The paper deals with a statistical approach to stability analysis in nonlinear stochastic programmin...
Keywords: Bayesian asymptotics Asymptotic normality Local asymptotic normality Locally asymptotic qu...
Most of statistical procedures consist in estimating parameters by minimizing (or maximizing) some c...