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Abstract In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized. The performances of the proposed estimators are evaluated mathematically by deriving the conditions in which the memory-type estimators would perform better than their corresponding conventional estimators under stratification. Through an extensive simulation, we evaluated the performance of the proposed estimators across various population parameters, revealing their enhanced efficiency in time-scaled survey. Additionally, a real data application is also used to support the mathematical findings, confirming the practical utility of the proposed estimators. The results of numerical study underscore the importance of the use of previous sampled information which significantly improves the accuracy and reliability of the proposed estimator for variance estimation for time-scaled surveys.

In statistical inference, introducing a probability distribution appropriate for modeling complex, skewed and symmetric datasets plays an important role. This article presents a new method, referred to as the exponential transformed approach, aimed at creating fresh probability models. This method entails transforming independent and identically distributed reduced Kies random variables. This article establishes various statistical and distributional properties of this model. Furthermore, the article employs several estimation methods to estimates the unknown parameter for the proposed model. Simulation experiments are conducted to showcase the effectiveness of the proposed estimators. Additionally, two real-world data analyses demonstrate practical applications in financial and production contexts, and it is shown that the recommended distribution has superior performance compared to other existing models.

Accelerated life tests play a very critical role in reliability analysis because highly reliable products are being produced with recent advanced technologies to sustain the market demand and competition. A progressive-stress accelerated life test is one of the kinds of accelerated life tests that allows applied stress to change continuously. Considering the importance of progressive-stress accelerated life tests, this paper deals with progressive-stress accelerated life tests when the lifetimes of units follow the log-logistic distribution and are progressively type-II censored where the associated scale parameter conforms to the inverse power law. Different estimators of model parameters are derived using maximum likelihood and Bayesian methods. Interval estimation is also considered. In the sequel, approximate confidence, bootstrap, and Bayes credible intervals are constructed. Bayes estimators are obtained under squared error, LINEX, and entropy loss functions using proper and improper prior distributions. A simulation study is conducted based on various censoring schemes. The coverage percentages and interval widths are computed via Monte Carlo simulations. Bayes predictive estimates and intervals are also obtained. Finally, two different accelerated life test data are analyzed for illustration purposes.

In the literature, modifying the baseline distributions and proposing novel approach of the existing probability models play an important role in the analysis of real-world phenomena as well as the development of new distributions essentially stems from the need to adequately characterize environmental and lifetime events. In this paper, we introduce a new model by applying maximum compound technique to the baseline distributions for modeling more types data used in different case of studies including symmetric, asymmetric, skewed and complex data sets. The proposed model called univariate maximum Harris extended Rayleigh distribution and three parameters characterize it, and it is regarded as strong competitor for widely applied symmetrical and non symmetrical models. The paper offers varied fundamental distributional and mathematical properties of the suggested distribution, such as conditional probability and its expectation, as well as quantile and moment-generating functions. The model’s parameters are estimated by applying numerous procedures including maximum likelihood estimation, Expectation–Maximization iterative method and Bayesian approach. Bayesian estimation is performed via various loss functions such as square error, Linex and general entropy functions. As well as, approximate and Bootstrap methods are used to construct the confidence interval for the unknown parameters of the proposed model. Additionally, we illustrated different extensive simulation experiments to see the potential of the suggested estimation methods which give satisfactory performance results. In the end, we applied two real data sets for illustrative purposes. In our illustration, we have compared the practicality of the recommended model with several well-known competing distributions.

Data analysis through probability-based methods plays an important role in decision-making in all spheres of life. Probability-based approaches lead to productive decision-making under uncertainty. To date, several probability distributions have been formulated that are characterized in different ways and successfully applied in various fields. Most of these probability distributions are studied by adding many additional parameters, typically from one to eight. There is the very least number of available distributional methodologies that are devised without inducing new parameters. In this integrated research piece of work, we provide a new cosine-adaptive method that is developed without considering any new parameters. We call the proposed method the weighted cosine-K family of distributions. Focusing on the proposed method, a new probability model is developed that is named the weighted cosine-Weibull (WC-Weibull) distribution. Some quartile-based characteristics along with estimators of the WC-Weibull distribution are derived. For the parameters of the WC-Weibull distribution, the point estimators are derived. The assessment of point estimators of the WC-Weibull distribution's parameters is provided through a simulation study. Finally, the utility of the WC-Weibull distribution is demonstrated by considering time-to-event phenomena in sports and the engineering sectors. Considering four statistical criteria, it has been shown that the WC-Weibull distribution is an efficient and optimal model for time-related facts (or events) in sports and engineering fields.

Abstract Background Venous thromboembolism (VTE) is a potentially life-threatening but preventable complication after urological surgery. Physicians are faced with the challenges of weighing the risks and benefits of thromboprophylaxis given scanty evidence for or against and practice variation worldwide. Objective The primary objective of the study was to explore the possibility of a risk-stratified approach for thromboembolism prophylaxis following radical prostatectomy. Design, setting, and participants A prospective database was accessed to cross-link venous thromboembolism events in 522 men who underwent minimally invasive prostatectomy between February 2010 and October 2021. A deterministic data linkage method was used to record events through electronic systems. Community Health Index (CHI) numbers were used to identify patients via electronic health records. Patient demographics and clinical characteristics such as age, comorbidities, Gleason staging, and readmission details accrued. Outcomes VTE within 90 days and development of a risk-stratified scoring system. All statistical analysis was performed using R-Statistical Software and the risk of VTE within 90 days of surgery was estimated via gradient-boosting decision trees (BRT) model. Results and limitations 1.1% (6/522) of patients developed deep vein thrombosis or pulmonary embolism within 3 months post-minimally invasive prostatectomy. Statistical analysis demonstrated a significant difference in the body mass index (p = 0.016), duration of hospital stay (p

This paper proposed a new biased proportional hazard regression (PHR) estimator which is the combination of elastic net proportional hazard regression (ENPHR) and principal components proportional hazard regression (PCPHR) estimator. Comparison of proposed estimator with ENPHR, PCPHR, ridge PHR, lasso PHR, r−k class PHR and maximum likelihood (ML) estimators is done in terms of scalar mean square error (MSE). Simulation study is conducted to examine the performance of each estimator. Furthermore, the developed estimator is utilized to analyze the infant mortality in Delhi, India.

A new software package, ISEtools, is introduced for use within the popular open-source programming language R that allows Bayesian statistical data analysis techniques to be implemented in a straightforward manner. Incorporating all collected data simultaneously, this Bayesian approach naturally accommodates sensor arrays and provides improved limit of detection estimates, including providing appropriate uncertainty estimates. Utilising >1500 lines of code, ISEtools provides a set of three core functions—loadISEdata, describeISE, and analyseISE— for analysing ion-selective electrode data using the Nikolskii−Eisenman equation. The functions call, fit, and extract results from Bayesian models, automatically determining data structures, applying appropriate models, and returning results in an easily interpretable manner and with publication-ready figures. Importantly, while advanced statistical and computationally intensive methods are employed, the functions are designed to be accessible to non-specialists. Here we describe basic features of the package, demonstrated through a worked environmental application.