Invitation to the seminar by Professor Lynn Roy LaMotte

We invite you to the lecture entitled Inverse prediction from multivariate, heteroscedastic responses, which will be delivered by Professor Lynn Roy LaMotte (USA) on Thursday, 1 December 2022 at 10:00 a.m., in the meeting room of the Institute of Measurement Science of the Slovak Academy of Sciences.

Professor Lynn Roy LaMotte — Professor Emeritus in the Biostatistics Program at Louisiana State University (LSU), Health Sciences Center, School of Public Health, in New Orleans, Louisiana, USA. His main research activities include statistical analysis, statistical modeling, applied statistics, data analysis, mathematical statistics, multivariate statistics, multivariate data analysis, linear regression, statistical inference, logistic regression, regression analysis, mixed linear models, linear estimation, categorical response, forensic statistics.

Inverse prediction from multivariate, heteroscedastic responses

Abstract

Inverse prediction is a method for inferring, from a subject’s response, what population that subject came from. To be useful, such inference must include assessment of variability, like a confidence set or a p-value table or graph.
Example: based on training data from insect larvae of known ages, and given measurements from a single mystery specimen, estimate the age of the mystery specimen.
Example: based on measurements of biparietal diameter and femur length from 1100 ultrasound scans of fetuses in vivo of known gestational ages, and given the measurements from an USS of a fetus of unknown GA, guess its GA.
Example: given measurements of percent DNA methylation at two genetic loci from saliva samples of 91 persons of known ages, guess the age of a mystery specimen.
Models for such processes typically need to be flexible, multivariate, and allow for variance-covariance matrices that change with age. I’ll describe the statistical setting, how that can be managed in widely-available statistical software, like SAS, BMDP, Stata, SPSS, and R, and give graphical examples.