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International Journal of Statistics and Probability
Number of Followers: 3

This is an Open Access Journal

Open Access journal
ISSN (Print) 1927-7032 - ISSN (Online) 1927-7040
Published by CCSE

[43 journals]

Reviewer Acknowledgements for International Journal of Statistics and
Probability, Vol. 14, No. 1
- PubDate: Wed, 02 Apr 2025 13:11:58 +000
Comparisons of Supervised Artificial Neural Networks With Population-Based
Statistical Probability Models in Moderate Sized Samples
- Abstract: Some of the basic issues affecting the comparison of population based statistical models and data-centric artificial neural network machine learning models are reviewed in moderate sized data samples. Comparisons of artificial neural networks and population-based models should consider and reflect both the data-centric and probability based nature of the models being compared. This is examined in a series of examples. Some guidelines for developing useful comparative settings are given. Improving the understandability of machine learning methods is an important goal.
  PubDate: Wed, 02 Apr 2025 13:11:34 +000
Algorithmic Construction of Bayesian Optimal Block Designs Using the
Linear Mixed Effects Model
- Abstract: In this paper a numerical method for construction of optimal Bayesian block designs of size two is considered. The main focus is on implementing prior information on the unknown error variance and variance of random block effects to calculate the A- and D-optimal designs. It is noted from the numerical results that the A- and D-optimal Bayesian block designs are insensitive to the shape of the prior distributions.
  PubDate: Wed, 02 Apr 2025 13:10:47 +000
The Kumaraswamy Log Composite Distribution Based on the Median: Inference
and Applications
- Abstract: In this paper a new univariate distribution with bounded support on the Kumaraswamy distribution is defined. Some of its main properties are studied, and a Monte Carlo simulation is implemented to evaluate the behavior of the maximum likelihood estimators. Two applications to real data sets are presented. In particular goodness of fit and quantile regression are show in order to illustrate the potential and flexibility of this new distribution.
  PubDate: Wed, 02 Apr 2025 13:10:14 +000
Comparing Two Independent Groups: Inferences About the Lower and Upper
Tails of the Distribution of D = X − Y
- Abstract: When comparing two independent groups, a way of getting a more detailed understanding of how the groups compare is to focus on multiple quantiles rather than a single measure of location. There are two distinct approaches regarding how this might be done. The first is to estimate a collection of quantiles for each group and choose an appropriate inferential method for comparing them. This approach has been studied extensively. For example, Doksum and Sievers (1976) derived a nonparametric method for computing confidence intervals for the difference between all quantiles for which the simultaneous probability coverage can be determined exactly assuming random sampling only. An analog of the Doksum–Sievers method was derive by Lombard (2005), which is based on the marginal distributions of two dependent groups. Other methods and applications are described in Wilcox (2023).Let X and Y denote two independent random variables and let D = X − Y. Note that under general conditions, the q quantile of D is not equal to the q quantile of X minus the q quantile of Y. Methods for making inferences about the median of D have been studied that have a close connection to the Wilcoxon–Mann–Whitney test. Here, four methods are suggested and studied via simulations that are aimed at making inferences about the tails of the distribution D with the goal of providing a deeper and more nuanced understanding of how groups compare.
  PubDate: Wed, 02 Apr 2025 13:08:21 +000
Modeling HIV/AIDS Progression: A Comparative Analysis of the 3-Parameter
Weibull, AFT, and Cox Proportional Hazards Models
- Abstract: This study models HIV/AIDS progression using the 3-Parameter Weibull Model and its adaptations, specifically the Accelerated Failure Time (AFT) Weibull and Cox Proportional Hazards (PH) models, to compare outcomes across age groups (20-30, 30-40, 40-50, 50-60, and over 60) and genders. Key performance metrics included Z-statistics, P-values, AD values, and standard errors to evaluate model fit and accuracy. The 3-Parameter Weibull model’s flexibility for time-varying hazards makes it well-suited for chronic conditions influenced by antiretroviral therapy (ART) and demographic factors.Results showed that the AFT model captured ART effects effectively in the 50-60 age group, particularly among males, while its predictive power decreased for younger cohorts, where ART’s impact was less pronounced. The Cox PH model, although interpretable, struggled in dynamic hazard rate scenarios, performing moderately in stable age groups but limited in detecting ART effects overall. The 3-Parameter Weibull model showed a strong fit in the 40-50 and 50-60 groups, with significant metrics affirming ART’s impact on survival, though predictive precision declined for those over 60. These findings highlight the complementary strengths of the AFT and 3- Parameter Weibull models, suggesting their integrated use can enhance state-specific modeling of HIV/AIDS progression.
  PubDate: Mon, 31 Mar 2025 23:59:44 +000

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