Pages that link to "Item:Q1301590"
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The following pages link to On robust Bayesian analysis for location and scale parameters (Q1301590):
Displaying 22 items.
- A note on Bayesian robustness for count data (Q424822) (← links)
- Bayesian heavy-tailed models and conflict resolution: a review (Q447980) (← links)
- Full robustness in Bayesian modelling of a scale parameter (Q907994) (← links)
- Regularized parameter estimation of high dimensional distribution (Q1015875) (← links)
- Bayesian inference in location-scale distributions with independent bivariate priors (Q1367091) (← links)
- The scaled beta2 distribution as a robust prior for scales (Q1699677) (← links)
- The location-scale mixture exponential power distribution: a Bayesian and maximum likelihood approach (Q1951226) (← links)
- A note on linearly constrained Bayes estimator in elliptical models (Q2196056) (← links)
- Modelling conflicting information using subexponential distributions and related classes (Q2393158) (← links)
- Compound and scale mixture of matricvariate and matrix variate Kotz-type distributions (Q2510918) (← links)
- A new class of multivariate distributions: scale mixture of Kotz-type distributions (Q2575553) (← links)
- Bayesian robustness modelling of location and scale parameters (Q2911693) (← links)
- (Q3002837) (← links)
- Bivariate Kurtotic Distributions of Garment Fibre Data (Q3435766) (← links)
- The Kotz-type distribution with applications (Q4454279) (← links)
- (Q4511007) (← links)
- A robustness evaluation of Bayesian tests for longitudinal data (Q5057352) (← links)
- The multivariate asymmetric slash Laplace distribution and its applications (Q5148601) (← links)
- A Generalization of the Univariate Slash by a Scale-Mixtured Exponential Power Distribution (Q5436411) (← links)
- Local Robustness in Bayesian Analysis (Q5747421) (← links)
- Robust Bayesian inference on scale parameters (Q5935555) (← links)
- Efficient MCMC estimation of some elliptical copula regression models through scale mixtures of normals (Q6574595) (← links)
