Nicholas Syring

Works (7)

Updated: January 3rd, 2024 05:02

2023 journal article

Gibbs posterior concentration rates under sub-exponential type losses

BERNOULLI, 29(2), 1080–1108.

By: N. Syring* & R. Martin n

author keywords: Classification; generalized Bayes; high-dimensional problem; M-estimation; model misspecification
TL;DR: This work provides simple sufficient conditions for establishing Gibbs posterior concentration rates when the loss function is of a sub-exponential type and applies these techniques in an important problem in medical statistics, namely, estimation of a personalized minimum clinically important difference. (via Semantic Scholar)
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Source: Web Of Science
Added: April 4, 2023

2022 article

Direct Gibbs posterior inference on risk minimizers: Construction, concentration, and calibration

ADVANCEMENTS IN BAYESIAN METHODS AND IMPLEMENTATION, Vol. 47, pp. 1–41.

By: R. Martin n & N. Syring*

author keywords: Asymptotics; Empirical risk minimization; Bayesian inference; Learning rate; M-estimation; Model misspecification; Statistical learning
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Source: Web Of Science
Added: January 2, 2024

2020 journal article

ROBUST AND RATE-OPTIMAL GIBBS POSTERIOR INFERENCE ON THE BOUNDARY OF A NOISY IMAGE

ANNALS OF STATISTICS, 48(3), 1498–1513.

By: N. Syring* & R. Martin n

author keywords: Adaptation; boundary detection; likelihood-free inference; model misspecification; posterior concentration rate
TL;DR: A robust Gibbs approach is developed that constructs a posterior distribution for the image boundary directly, without modeling the pixel intensities, and it is proved that, for a suitable prior on theimage boundary, the Gibbs posterior concentrates asymptotically at the minimax optimal rate, adaptive to the boundary smoothness. (via Semantic Scholar)
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Added: August 10, 2020

2019 journal article

Gibbs posterior inference on value-at-risk

SCANDINAVIAN ACTUARIAL JOURNAL, (7), 548–557.

By: N. Syring*, L. Hong * & R. Martin n

author keywords: Direct posterior; discrepancy function; M-estimation; model misspecification; risk capital; robust estimation
TL;DR: This paper links data and VaR directly via what is called a discrepancy function, and this leads naturally to a Gibbs posterior distribution for VaR that does not suffer from the aforementioned biases and inefficiencies. (via Semantic Scholar)
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Added: August 12, 2019

2019 journal article

Miscellanea Calibrating general posterior credible regions

BIOMETRIKA, 106(2), 479–486.

By: N. Syring* & R. Martin n

author keywords: Bootstrap; Coverage probability; Gibbs posterior distribution; Model misspecification; Stochastic approximation
TL;DR: A scalar tuning parameter is introduced that controls the posterior distribution spread, and a Monte Carlo algorithm is developed that sets this parameter so that the corresponding credible region achieves the nominal frequentist coverage probability. (via Semantic Scholar)
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Added: November 11, 2019

2017 journal article

Efficient simulation from a gamma distribution with small shape parameter

COMPUTATIONAL STATISTICS, 32(4), 1767–1775.

By: C. Liu *, R. Martin n & N. Syring  n

author keywords: Acceptance rate; Acceptance-rejection method; Asymptotic distribution; Exponential distribution; R software
Source: Web Of Science
Added: August 6, 2018

2017 journal article

Gibbs posterior inference on the minimum clinically important difference

JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 187, 67–77.

By: N. Syring n & R. Martin n

author keywords: Clinical significance; Loss function; M-estimation; Model-free inference; Posterior convergence rate
TL;DR: A likelihood-free posterior distribution is constructed for the minimum clinically important difference (MCID) and it is shown, numerically, that an appropriately scaled version the posterior yields interval estimates for the MCID which are both valid and efficient even for relatively small sample sizes. (via Semantic Scholar)
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Added: August 6, 2018

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