2010/12/30 by Teo Sharia, Sharia, Teo
Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Mathematical Approximation and Integration #Methodology (stat.ME) #Probability (math.PR) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1101.0031
openalex publication_date 2010/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we propose a wide class of truncated stochastic approximation procedures with moving random bounds. While we believe that the proposed class of procedures will find its way to a wider range of applications, the main motivation is to accommodate applications to parametric statistical estimation theory. Our class of stochastic approximation procedures has three main characteristics: truncations with random moving bounds, a matrix valued random step-size sequence, and dynamically changing random regression function. We establish convergence and consider several examples to illustrate the results.