Optimal Sampling for Generalized Linear Models Under Measurement Constraints
نویسندگان
چکیده
منابع مشابه
Optimal Estimation under Linear Constraints
This paper shows how a multivariate Bayes estimator can be adjusted to satisfy a set of linear constraints. In the direct approach, the constraint is enforced by a restriction on the class of admissible estimators. In an alternative approach, the constraint is merely encouraged by a mixed risk function which penalises misbalance between the estimator and the constraint. The adjustment to the op...
متن کاملD-optimal Factorial Designs under Generalized Linear Models
Generalized linear models (GLMs) have been used widely for modelling the mean response both for discrete and continuous random variables with an emphasis on categorical response. Recently Yang, Mandal and Majumdar (2013) considered full factorial and fractional factorial locally D-optimal designs for binary response and two-level experimental factors. In this paper, we extend their results to a...
متن کاملOptimal Designs for Generalized Linear Models
Both HK1 and HK2 deal with experiments in which the planned analysis is based on a linear model. Selecting designs for such experiments remains a critically important problem. However, there are many problems for which a linear model may not be a great choice. For example, if the response is a binary variable or a count variable rather than a continuous measurement, a linear model may be quite ...
متن کاملD-Optimal Design for Generalized Linear Models
D-optimality is one of the most commonly used design criteria for linear regression models. In industrial experiments binary or count data often arise, for example defective/nondefective or number of defects. For such data Generalized Linear Models (GLMs) are appropriate. An analogous D-optimality design criterion can be developed using the asymptotic covariance matrix. For GLMs, this matrix is...
متن کاملMinimax Linear Regression under Measurement Constraints
We consider the problem of linear regression under measurement constraints and derive computationally feasible subsampling strategies to sample a small portion of design (data) points in a linear regression model y = Xβ + ε. The derived subsampling algorithms are minimax optimal for estimating the regression coefficients β under the fixed design setting, up to a small (1 + ) relative factor. Ex...
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: Journal of Computational and Graphical Statistics
سال: 2020
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2020.1778483