Elements of Computational Statistics

Gentle, James E.

doi:10.1007/b97337

Cited by 28 publications

(1 citation statement)

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“…More precisely, it is necessary to initially derive the latent dataset composed of n samples of N ( 0, ∑). As proposed in [20], the starting point is to compute, by applying the Cholesky factorization, a matrix B such that BB T = ∑. Hence, numerical sampling x i ∈ R m ; 1 ≤ i ≤ n, of N ( 0, Σ) can be derived as x i = µ + Bz i , where µ = 0 is the m dimensional null vector, since all m expected values are equal to zero, and z i are independent samplings of a univariate standard normal distribution N (0, 1).…”

Section: Methodology For Data Generationmentioning

confidence: 99%

A Methodology for Controlling Bias and Fairness in Synthetic Data Generation

et al. 2022

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The development of algorithms, based on machine learning techniques, supporting (or even replacing) human judgment must take into account concepts such as data bias and fairness. Though scientific literature proposes numerous techniques to detect and evaluate these problems, less attention has been dedicated to methods generating intentionally biased datasets, which could be used by data scientists to develop and validate unbiased and fair decision-making algorithms. To this end, this paper presents a novel method to generate a synthetic dataset, where bias can be modeled by using a probabilistic network exploiting structural equation modeling. The proposed methodology has been validated on a simple dataset to highlight the impact of tuning parameters on bias and fairness, as well as on a more realistic example based on a loan approval status dataset. In particular, this methodology requires a limited number of parameters compared to other techniques for generating datasets with a controlled amount of bias and fairness.

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Section: Methodology For Data Generationmentioning

confidence: 99%