Estimating linear mixed effect models with non-normal random effects through saddlepoint approximation and its application in retail pricing analytics.

ABSTRACT: Linear Mixed Effects (LME) models are powerful statistical tools that have been employed in many different real-world applications such as retail data analytics, marketing measurement, and medical research. Statistical inference is often conducted via maximum likelihood estimation with Normality assumptions on the random effects. Nevertheless, for many applications in the retail industry, it is often necessary to consider non-Normal distributions on the random effects when considering the unknown parameters' business interpretations. Motivated by this need, a linear mixed effects model with possibly non-Normal distribution is studied in this research. We propose a general estimating framework based on a saddlepoint approximation (SA) of the probability density function of the dependent var

SUBMITTER: Chen H 

PROVIDER: S-EPMC11328820 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature