activity
20082024
most citedAsymptotics in response-adaptive designs generated by a two-color, randomly reinforced urn

61 citations · 113 across the 9 of their papers we have counts for

collaborators

9 papers

stat.ME2024

Optimal design of experiments for functional linear models with dynamic factors

Caterina May, Theodoros Ladas, Davide Pigoli +1

In this work we build optimal experimental designs for precise estimation of the functional coefficient of a function-on-function linear regression model where both the response an…

stat.ME2024★ 1 cited

Maximum Likelihood Estimation under the Emax Model: Existence, Geometry and Efficiency

Giacomo Aletti, Nancy Flournoy, Caterina May +1

This study focuses on the estimation of the Emax dose-response model, a widely utilized framework in clinical trials, agriculture, and environmental experiments. Existing challenge…

math.ST2018★ 2 cited

The Effects of Adaptation on Inference for Non-Linear Regression Models with Normal Errors

Nancy Flournoy, Caterina May, Chiara Tommasi

This work studies the properties of the maximum likelihood estimator (MLE) of a non-linear model with Gaussian errors and multidimensional parameter. The observations are collected…

math.ST2014★ 2 cited

Best estimation of functional linear models

Giacomo Aletti, Caterina May, Chiara Tommasi

Observations which are realizations from some continuous process are frequent in sciences, engineering, economics, and other fields. We consider linear models, with possible random…

stat.ME2012★ 6 cited

KL-optimum designs: theoretical properties and practical computation

Giacomo Aletti, Caterina May, Chiara Tommasi

In this paper some new properties and computational tools for finding KL-optimum designs are provided. KL-optimality is a general criterion useful to select the best experimental c…

stat.ME2011★ 2 cited

An adaptive sequential optimum design for model selection and parameter estimation in non-linear nested models

Caterina May, Chiara Tommasi

This paper has been withdrawn by the author because it has been substantially modified.