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- Generalized fiducial inference for the generalized logistic . . .
This article primarily considers the statistical inference of the scale parameter, shape parameter, and reliability of the generalized logistic distribution in both censored and uncensored cases
- Generalized Fiducial Inference for the Stress–Strength . . . - MDPI
This article focuses on the statistical inference for the stress–strength parameter R=P (Y<X) of the generalized logistic distribution with the same and different scale parameters
- Generalized Fiducial Inference for Binary Logistic Item Response Models
In the current paper, we apply GFI to a family of binary logistic item response theory models, which includes the two-parameter logistic (2PL), bifactor and exploratory item factor models as special cases Asymptotic properties of the resulting fiducial distribution are discussed
- Generalized Fiducial Inference: A Review and New Results
The resulting generalized fiducial distribution (GFD) is a data-dependent distribution on the parameter space GFD can be viewed as a distribution estimator (as opposed to a point or interval estimator) of the unknown parameter of interest
- Generalized fiducial inference for the generalized logistic . . .
Generalized fiducial inference for the generalized logistic distribution: Censored and uncensored cases
- Generalized fiducial inference for the generalized logistic
This article primarily considers the statistical inference of the scale parameter, shape parameter, and reliability of the generalized logistic distribution in both censored and uncensored cases
- [2302. 14598] Introduction to Generalized Fiducial Inference
In this chapter, we illuminate the usefulness of the fiducial philosophy, introduce the definition of a generalized fiducial distribution, and apply it to interesting, non-trivial inferential examples
- On a Randomly Censoring Scheme for Generalized Logistic Distribution . . .
This paper addresses the need for effective inferential procedures for generalized logistic distribution under a random censoring model, and it is motivated by the challenges posed by the randomly censored data commonly encountered in fields like medical research and reliability engineering
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