Computing the Bayesian highest posterior density credible sets for the lognormal mean

Dalpatadu, Rohan J.; Gewali, Laxmi; Singh, Ashok K.

doi:10.1002/env.547

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Some researchers, who hesitate to use T -or F-distributions, prefer to use Bayesian approaches for statistical inference in LME models. Likewise, a Bayesian approach with credible intervals [38][39][40] can extended to construct Bayesian SCBs in LME models. However, the comparison of Bayesian credible sets to frequentist confidence intervals is not straightforward.…”

Section: Resultsmentioning

confidence: 99%

Effective simultaneous confidence bands for repeated measurements in linear mixed-effect models

Mun

Chun

2013

Journal of Statistical Computation and Simulation

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Linear mixed-effect (LME) models have been extensively accepted to analyse repeated measurements due to their flexibility and ability to handle subject-specific matters. The inclusion of random effects has resulted in much benefit with respect to estimation, but it is complicated to measure their impact on hypothesis testing. While the same complication is present in the construction of simultaneous confidence bands (SCBs), degrees of freedom (df) for SCBs have rarely been discussed unlike those for test statistics. This motivates us to propose the adoption of approximate df to construct SCBs in LME models. Simulation studies were performed to compare the performances of different calculations for the df. The results of simulations demonstrate the efficacy of the use of approximate df. In addition, our proposal allows linesegment SCBs developed under covariance models to function with LME models. Applications with real longitudinal datasets present consistent results with the simulation study.

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Section: Resultsmentioning

confidence: 99%

Effective simultaneous confidence bands for repeated measurements in linear mixed-effect models

Mun

Chun

2013

Journal of Statistical Computation and Simulation

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“…The highest posterior density (HPD) interval as the interval estimator in the Bayesian method is used to credible confidence intervals for MCMC estimates. For more information, see Turkkan and Pham-Gia [24] and Chen and Shao [25,26].…”

Section: Markov Chain Monte Carlo Methodsmentioning

confidence: 99%

Inference of Reliability Analysis for Type II Half Logistic Weibull Distribution with Application of Bladder Cancer

Mohamed

Tolba

Almetwally

et al. 2022

Axioms

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The estimation of the unknown parameters of Type II Half Logistic Weibull (TIIHLW) distribution was analyzed in this paper. The maximum likelihood and Bayes methods are used as estimation methods. These estimators are used to estimate the fuzzy reliability function and to choose the best estimator of the fuzzy reliability function by comparing the mean square error (MSE). The simulation’s results showed that fuzziness is better than reality for all sample sizes, and fuzzy reliability at Bayes predicted estimates is better than the maximum likelihood technique. It produces the lowest average MSE until a sample size of n = 50 is obtained. A simulated data set is applied to diagnose the performance of the two techniques applied here. A real data set is used as a practice for the model discussed and developed the maximum likelihood estimate alternative model of TIIHLW as Topp Leone inverted Kumaraswamy, modified Kies inverted Topp–Leone, Kumaraswamy Weibull–Weibull, Marshall–Olkin alpha power inverse Weibull, and odd Weibull inverted Topp–Leone. We conclude that the TIIHLW is the best distribution fit for this data.

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“…Sophisticated modelling and three-dimensional visualisation packages (i.e. SWISS-PROT [40], SCOP [41] and UCL [42]) and software for investigating protein-ligand complexes (LIGPLOTS [43]) can assist the identification of the possible interactions of the new affinity ligand. Moreover, automated docking programs (DOCK [44] and LUDI [45]) allow prediction of the structure, mode and free energy of the binding of ligand-protein complexes, while the generation of possible ligands can be exploited (HOOK [46]) by using chemical and steric characteristics of the proteinbinding site.…”

Section: Design Of the Tailor-made Peptidementioning

confidence: 99%

Antibody-Like Peptides as a Novel Purification Tool for Drugs Design

Tozzi¹,

Giraudi²

2006

CPD

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New pharmaceutical approaches, such as biotechnology industry, genomic and proteomic studies, require the development of new analytical and preparative tools that should allow the resolution and the characterisation of complex sets of molecule mixtures in a high-throughput mode with the isolation of a single substance from complex matrices in a high degree of purity, low costs and wide availability. In this review we discuss the design of a tailor-made peptide by focusing our attention on the bioinformatic techniques and combinatorial approaches. Then synthesis, purification and characterisation of peptides with recognition properties are discussed by considering different approaches and their fitness to drug design. Moreover, the development of affinity devices and the discussion of their characteristics to optimise the purification phase are reported. Applications of peptide ligands that bind pharmaceutical compounds (i.e. therapeutic proteins, monoclonal antibodies, hormones) are described and analytical tools mentioned and evaluated. Future perspectives, in particular alternative applications of peptide ligands and their substitution with peptidomimetic compounds, are described.

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Computing the Bayesian highest posterior density credible sets for the lognormal mean

Cited by 5 publications

References 3 publications

Effective simultaneous confidence bands for repeated measurements in linear mixed-effect models

Effective simultaneous confidence bands for repeated measurements in linear mixed-effect models

Inference of Reliability Analysis for Type II Half Logistic Weibull Distribution with Application of Bladder Cancer

Antibody-Like Peptides as a Novel Purification Tool for Drugs Design

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