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Optimal subsampling designs

Imberg, Henrik, 1991 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Axelson-Fisk, Marina, 1972 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Jonasson, Johan, 1966 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
 (creator_code:org_t)
2023
2023
Engelska.
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
Abstract Ämnesord
Stäng  
  • Subsampling is commonly used to overcome computational and economical bottlenecks in the analysis of finite populations and massive datasets. Existing methods are often limited in scope and use optimality criteria (e.g., A-optimality) with well-known deficiencies, such as lack of invariance to the measurement-scale of the data and parameterisation of the model. A unified theory of optimal subsampling design is still lacking. We present a theory of optimal design for general data subsampling problems, including finite population inference, parametric density estimation, and regression modelling. Our theory encompasses and generalises most existing methods in the field of optimal subdata selection based on unequal probability sampling and inverse probability weighting. We derive optimality conditions for a general class of optimality criteria, and present corresponding algorithms for finding optimal sampling schemes under Poisson and multinomial sampling designs. We present a novel class of transformation- and parameterisation-invariant linear optimality criteria which enjoy the best of two worlds: the computational tractability of A-optimality and invariance properties similar to D-optimality. The methodology is illustrated on an application in the traffic safety domain. In our experiments, the proposed invariant linear optimality criteria achieve 92-99% D-efficiency with 90-95% lower computational demand. In contrast, the A-optimality criterion has only 46% and 60% D-efficiency on two of the examples.

Ämnesord

NATURVETENSKAP  -- Matematik -- Beräkningsmatematik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Computational Mathematics (hsv//eng)
NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

L-optimality
inverse probability weighting
A-optimality
D-optimality
unequal probability sampling
M-estimation

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art (ämneskategori)
vet (ämneskategori)

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