Výsledky vyhledávání - "Data generating process"

Akademický článek

The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration

Autor: Peter C. Austin, Douglas S. Lee, Bo Wang

Publikováno v: Diagnostic and Prognostic Research, Vol 8, Iss 1, Pp 1-18 (2024)

Abstract Background Machine learning methods are increasingly being used to predict clinical outcomes. Optimism is the difference in model performance between derivation and validation samples. The term “data hungriness” refers to the sample size

Externí odkaz: https://doaj.org/article/2757b5a917974bd4952970a2d5f6c368

Zobrazit plný text záznamu

Kniha

Dynamics of decision making: from evidence to preference and belief.

Autor: Marius Usher

Externí odkaz: Kolekce e-knih KNAV

Akademický článek

Determining dependence among random variables across observations

Autor: Peter J. Veazie

Publikováno v: Ratio Mathematica, Vol 51, Iss 0 (2024)

It is common in applied research to analyze data from data generating processes with dependencies among random variables across observations. Such dependencies impact power calculations and standard errors. However, it is also common to mistake the s

Externí odkaz: https://doaj.org/article/b178c72e29eb46b1bcce180a57995130

Zobrazit plný text záznamu

Akademický článek

The iterative bisection procedure: a useful tool for determining parameter values in data-generating processes in Monte Carlo simulations

Autor: Peter C. Austin

Publikováno v: BMC Medical Research Methodology, Vol 23, Iss 1, Pp 1-10 (2023)

Abstract Background Data-generating processes are key to the design of Monte Carlo simulations. It is important for investigators to be able to simulate data with specific characteristics. Methods We described an iterative bisection procedure that ca

Externí odkaz: https://doaj.org/article/fbf4566fa653486d9e46a315c63f2091

Zobrazit plný text záznamu

Plný text ve formátu HTML

Elektronická kniha

Eminence of Models

Autor: Zhu, Mu, author

Publikováno v: Essential Statistics for Data Science : A Concise Crash Course, 2023, ill.

Externí odkaz: https://doi.org/10.1093/oso/9780192867735.003.0001

Zobrazit plný text záznamu

Akademický článek

Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data

Autor: Karsten Lübke, Matthias Gehrke, Jörg Horst, Gero Szepannek

Publikováno v: Journal of Statistics Education, Vol 28, Iss 2, Pp 133-139 (2020)

Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclu

Externí odkaz: https://doaj.org/article/6a4f74a7f3794e278923846e55e7dbc0

Zobrazit plný text záznamu