Linear Minimax Regret Estimation of Deterministic Parameters with Bounded Data Uncertainties
نویسندگان
چکیده
منابع مشابه
Minimax Estimation of Linear Combinations of Restricted Location Parameters
Discussion Papers are a series of manuscripts in their draft form. They are not intended for circulation or distribution except as indicated by the author. For that reason Discussion Papers may not be reproduced or distributed without the written consent of the author. The estimation of a linear combination of several restricted location parameters is addressed from a decision-theoretic point o...
متن کاملEstimation of Bounded Model Uncertainties
We identify parameters of a given input-output model so that estimated model output is consistent with the measured output of the system modeled. Parameter estimation based on a set-membership approach is a nonprobabilistic method for characterizing the uncertainty with which each model parameter is known. The model is consistent with data if the estimated output domain contains measured system...
متن کاملMinimax Estimation of a Bounded Discrete Parameter
For a vast class of discrete model families with cdf’s Fθ, and for estimating θ under squared error loss under a constraint of the type θ ∈ [0,m], we present a general and unified development concerning the minimaxity of a boundary supported prior Bayes estimator. While the sufficient conditions obtained are of the expected form m ≤ m(F ), the approach presented leads, in many instances, to bot...
متن کاملMinimax Estimation of a Bounded Squared Mean
Consider a normal model with unknown mean bounded by a known constant. This paper deals with minimax estimation of the squared mean. We establish an expression for the asymptotic minimax risk. This result is applied in nonparametric estimation of quadratic functionals.
متن کاملRobust Logistic Regression with Bounded Data Uncertainties
Building on previous work in robust optimization, we present a formulation of robust logistic regression under bounded data uncertainties. The robust estimates are obtained using block coordinate gradient descent with iterative group thresholding, which zeros out highly uncertain variables. For high dimensional problems with uncertain measurements, we discuss the addition of regularization pena...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2004
ISSN: 1053-587X
DOI: 10.1109/tsp.2004.831144