6 citations · 13 across the 7 of their papers we have counts for
7 papers
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…
A sub-sampling algorithm preventing outliers
L. Deldossi, E. Pesce, C. Tommasi
Nowadays, in many different fields, massive data are available and for several reasons, it might be convenient to analyze just a subset of the data. The application of the D-optima…
Designing to detect heteroscedasticity in a regression model
Alessandro Lanteri, Samantha Leorato, Jesús López-Fidalgo +1
We consider the problem of designing experiments to detect the presence of a specified heteroscedastity in a non-linear Gaussian regression model. In this framework, we focus on th…
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…
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…
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…
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This paper has been withdrawn by the author because it has been substantially modified.