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D. Schmidt

13 papers hereh-index 338.2k citations124 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3
  • last author9

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • stat.ME7
  • stat.CO3
  • math.ST1
  • q-bio.GN1
  • stat.ML1
same name
  • D. Schmidt — 79 papers, h 37
  • D. Schmidt — 33 papers, h 31
  • D. Schmidt — 25 papers, h 35
  • D. Schmidt — 22 papers, h 20
  • D. Schmidt — 10 papers, h 16
  • D. Schmidt — 9 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142022
most citedA simple sampler for the horseshoe estimator

269 citations · 335 across the 12 of their papers we have counts for

collaborators
Showing 2022 · stat.MEShow all

4 papers · 2 filters

stat.ME2022

Introduction to minimum message length inference

Enes Makalic, Daniel F. Schmidt

The aim of this manuscript is to introduce the Bayesian minimum message length principle of inductive inference to a general statistical audience that may not be familiar with info…

stat.ME2022

Minimum message length inference of the Weibull distribution with complete and censored data

Enes Makalic, Daniel F. Schmidt

The Weibull distribution, with shape parameter k>0 and scale parameter λ>0, is one of the most popular parametric distributions in survival analysis with complete or censored d…

stat.ME2022★ 12 cited

Maximum likelihood estimation of the Weibull distribution with reduced bias

Enes Makalic, Daniel F. Schmidt

In this short note, we derive a new bias adjusted maximum likelihood estimate for the shape parameter of the Weibull distribution with complete data and type I censored data. The p…

stat.ME2022

MML Probabilistic Principal Component Analysis

Enes Makalic, Daniel F. Schmidt

Principal component analysis (PCA) is perhaps the most widely used method for data dimensionality reduction. A key question in PCA is deciding how many factors to retain. This manu…

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