Published in Automatica
A general analysis theory is presented which can incorporate robust stability or robust performance defined in H ∞ or μ framework for sequential design purposes. As a result, a methodology for sequential design of robust decentralized controllers is proposed. To do so, new formulations of linear fractional transformation of complementary sensitivit...
Published in Proceedings of the National Academy of Sciences of the United States of America
The evolution of proteins for novel functions involves point mutations and recombinations of domains or structural segments. Mimicking this process by rational design in vitro is still a major challenge. The present report demonstrates that the active site of the enzyme glutathione transferase (GST) A1-1 can be tailored for high catalytic efficienc...
A number of active site mutants of human Alpha class glutathione transferase A1-1 (hGST A1-1) were made and characterized to determine the structural determinants for alkenal activity. The choice of mutations was based on primary structure alignments of hGST A1-1 and the Alpha class enzyme with the highest alkenal activity, hGST A4-4, from three di...
Stallard (1998, Biometrics 54, 279-294) recently used Bayesian decision theory for sample-size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other h...
Published in Statistical Methods and Applications
We consider the situation where one wants to maximise a functionf(θ,x) with respect tox, with θ unknown and estimated from observationsyk. This may correspond to the case of a regression model, where one observesyk=f(θ,xk)+εk, with εk some random error, or to the Bernoulli case whereyk∈{0, 1}, with Pr[yk=1|θ,xk|=f(θ,xk). Special attention is given ...
Classical regression analysis is usually performed in two steps. In a first step an appropriate model is identified to describe the data generating process and in a second step statistical inference is performed in the identified model. An intuitively appealing approach to the design of experiment for these different purposes are sequential strateg...
We consider the situation where one wants to maximise a function f(theta,x) with respect to x, with theta unknown and estimated from observations y_k. This may correspond to the case of a regression model, where one observes y_k = f( theta,x_k) + epsilon _k, with epsilon_ k some random error, or to the Bernoulli case where y_k in {0,1}, with Pr[y_k...
We consider a parameter estimation problem with independent observations where one samples from a finite population of independent and identically distributed experimental conditions X. The size of the population is N but only n samples, a proportion alpha of N, can be used. The quality of a sample is measured by a regular optimality criterion phi(...
Under the condition that the design space is finite, new sufficient conditions for the strong consistency and asymptotic normality of the least-squares estimator in nonlinear stochastic regression models are derived. Similar conditions are obtained for the maximum-likelihood estimator in Bernoulli type experiments. Consequences on the sequential de...
Published in Metrika
We study the consistency of parameter estimators in adaptive designs generated by a one-step ahead D-optimal algorithm. We show that when the design space is finite, under mild conditions the least-squares estimator in a nonlinear regression model is strongly consistent and the information matrix evaluated at the current estimated value of the para...
