Efficient Robbins-Monro Procedure for Binary Data

The continuous dynamic Robbins-Monro procedure

Sequential Experimental Designs for Generalized Linear Models

We consider the problem of experimental design when the response is modeled by a generalized linear model (GLM) and the experimental plan can be determined sequentially. Most previous research on this problem has been limited either to one-factor, binary response experiments or to augmenting the design when there are already sufficient data to compute parameter estimates. We suggest a new proce...

@bullet Estimation following a Robbins-monro Designed Experiment

Unconstrained Recursive Importance Sampling

We propose an unconstrained stochastic approximation method of finding the optimal measure change (in an a priori parametric family) for Monte Carlo simulations. We consider different parametric families based on the Girsanov theorem and the Esscher transform (or exponentialtilting). In a multidimensional Gaussian framework, Arouna uses a projected Robbins-Monro procedure to select the paramete...

BUREAU OF THE CENSUS STATISTICAL RESEARCH DIVISION Statistical Research Report Series No. RR2001/02 Convergence of a Robbins-Monro Algorithm for Recursive Parameter Estimation with Non-Monotone Weights and Multiple Zeros

Convergence properties are established for the output of a deterministic Robbins-Monro recursion for functions that can have singularities and multiple zeros. Our analysis is built largely on adaptations of lemmas of Fradkov published in Russian. We present versions of these lemmas in English for the first time. A gap in Fradkov’s proof of the final lemma is fixed but only for the scalar case.