References of "Ghaderinezhad, Fatemeh"
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See detailThe Wasserstein Impact Measure (WIM): A practical tool for quantifying prior impact in Bayesian statistics
Ley, Christophe UL; Ghaderinezhad, Fatemeh; Serrien, Ben

in Computational Statistics and Data Analysis (2022), 174

The prior distribution is a crucial building block in Bayesian analysis, and its choice will impact the subsequent inference. It is therefore important to have a convenient way to quantify this impact, as ... [more ▼]

The prior distribution is a crucial building block in Bayesian analysis, and its choice will impact the subsequent inference. It is therefore important to have a convenient way to quantify this impact, as such a measure of prior impact will help to choose between two or more priors in a given situation. To this end a new approach, the Wasserstein Impact Measure (WIM), is introduced. In three simulated scenarios, the WIM is compared to two competitor prior impact measures from the literature, and its versatility is illustrated via two real datasets. [less ▲]

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See detailSome simple variance bounds from Stein’s method
Ley, Christophe UL; Daly, Fraser; Ghaderinezhad, Fatemeh et al

in ALEA: Latin American Journal of Probability and Mathematical Statistics (2021), 18

Using coupling techniques based on Stein’s method for probability approximation, we revisit classical variance bounding inequalities of Chernoff, Cacoullos, Chen and Klaassen. Our bounds are immediate in ... [more ▼]

Using coupling techniques based on Stein’s method for probability approximation, we revisit classical variance bounding inequalities of Chernoff, Cacoullos, Chen and Klaassen. Our bounds are immediate in any context wherein a Stein identity is available. After providing illustrative examples for a Gaussian and a Gumbel target distribution, our main contributions are new variance bounds in settings where the underlying density function is unknown or intractable. Applications include bounds for analysis of the posterior in Bayesian statistics, bounds for asymptotically Gaussian random variables using zero-biased couplings, and bounds for random variables which are New Better (Worse) than Used in Expectation. [less ▲]

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