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The study based on the estimation of the stress–strength reliability parameter plays a vital role in showing system efficiency. In this paper, considering independent strength and stress random variables distributed as inverted exponentiated Rayleigh model, the author have developed estimation procedures for the stress–strength reliability parameter R = P(X>Y) under Type II hybrid censored samples.

The maximum likelihood and Bayesian estimates of R based on Type II hybrid censored samples are evaluated. Because there is no closed form for the Bayes estimate, the author use the Metropolis–Hastings algorithm to obtain approximate Bayes estimate of the reliability parameter. Furthermore, the author construct the asymptotic confidence interval, bootstrap confidence interval and highest posterior density (HPD) credible interval for R. The Monte Carlo simulation study has been conducted to compare the performance of various proposed point and interval estimators. Finally, the validity of the stress–strength reliability model is demonstrated via a practical case.

The performance of various point and interval estimators is compared via the simulation study. Among all proposed estimators, Bayes estimators using MHG algorithm show minimum MSE for all considered censoring schemes. Furthermore, the real data analysis indicates that the splashing diameter decreases with the increase of MPa under different hybrid censored samples.

The frequentist and Bayesian methods are developed to estimate the associated parameters of the reliability model under the hybrid censored inverted exponentiated Rayleigh distribution. The application of the proposed stress–strength reliability model will help the reliability engineers and also other scientists to estimate the system reliability.

A progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior to the predetermined terminal time T. The generalized progressive hybrid censoring scheme (GPHCS) efficiently addresses to overcome the limitation of the PHCS.

In this article, estimation of model parameter, survival and hazard rate of the Unit-Lindley distribution (ULD), when sample comes from the GPHCS, have been taken into account. The maximum likelihood estimator has been derived using Newton–Raphson iterative procedures. Approximate confidence intervals of the model parameter and their arbitrary functions are established by the Fisher information matrix. Bayesian estimation procedures have been derived using Metropolis–Hastings algorithm under squared error loss function. Convergence of Markov chain Monte Carlo (MCMC) samples has been examined. Various optimality criteria have been considered. An extensive Monte Carlo simulation analysis has been shown to compare and validating of the proposed estimation techniques.

The Bayesian MCMC approach to estimate the model parameters and reliability characteristics of the generalized progressive hybrid censored data of ULD is recommended. The authors anticipate that health data analysts and reliability professionals will get benefit from the findings and approaches presented in this study.

The ULD has a broad range of practical utility, making it a problem to estimate the model parameters as well as reliability characteristics and the significance of the GPHCS also encourage the authors to consider the present estimation problem because it has not previously been discussed in the literature.

The purpose of this study is to contribute to the literature on information elicitation. The authors investigated the impact of social influence strategies on eyewitness recall performance. Specifically, the authors examined the effect of social influence techniques (Cialdini, 2007) on recall performance (Experiment 1) and conducted a follow-up experiment to examine the incremental effect of social proof on the report everything cognitive interview mnemonic (Experiment 2).

Participants watched a video depicting vandalism (Experiment 1: N = 174) or a verbal altercation (Experiment 2: N = 128) and were asked to recall the witnessed event. Experiment 1: Participants were assigned randomly to one of six conditions: control (open-ended prompt), engage and explain (interview ground rules), consistency (signing an agreement to work diligently), reciprocity (given water and food), authority (told of interviewer’s training) and social proof (shown transcript from an exemplar participant). Experiment 2: The authors used a 2 (social proof: present, absent) × 2 (report everything: present, absent) between-participants design.

Across both experiments, participants exposed to the social proof tactic (i.e. compared to a model exemplar) spoke longer and recalled more correct details than participants not exposed to the social proof tactic. In Experiment 2, participants interviewed with the report everything mnemonic also spoke longer, recalled more correct details, more incorrect details and provided slightly more confabulations than those not interviewed with the report everything mnemonic.

The findings have practical value for police investigators and other professionals who conduct interviews (e.g. military personnel, doctors obtaining information from patients). Interviewers can incorporate social proof in their interviewing practices to help increase the amount and accuracy of information obtained.

The authors explore the hypothesis that some movements in commodity prices are anticipated (news shocks) and can trigger aggregate fluctuations in small open emerging economies. This paper aims to discuss the aforementioned objective.

The authors build a multi-sector dynamic stochastic general equilibrium model with endogenous commodity production. There are five exogenous processes: a country-specific interest rate shock that responds to commodity price fluctuations, a productivity (TFP) shock for each sector and a commodity price shock. Both TFP and commodity price shocks are composed of unanticipated and anticipated components.

The authors show that news shocks to commodity prices lead to higher output, investment and consumption, and a countercyclical movement in the trade-balance-to-output ratio. The authors also show that commodity price news shocks explain about 24% of output aggregate fluctuations in the small open economy.

Given the importance of both anticipated and unanticipated commodity price shocks, policymakers should pay attention to developments in commodity markets when designing policies to attenuate the business cycles. Future research should investigate the design of optimal fiscal and monetary policies in SOE subject to news shocks in commodity prices.

This paper contributes to the knowledge of the sources of fluctuations in emerging economies highlighting the importance of a new source: news shocks in commodity prices.

This study examined subjective numeracy and its relationship with accounting judgements on probability issues.

A subjective numeracy scale (SNS) questionnaire was distributed to 231 accounting students to measure self-evaluated numeracy. Modified Bayesian reasoning tasks were applied in an accounting-related probability estimation, manipulating presentation formats.

The study revealed a positive relationship between self-evaluated numeracy and performance in accounting probability estimation. The findings suggest that switching the format of probability expressions from percentages to frequencies can improve the performance of participants with low self-evaluated numeracy.

Adding objective numeracy measurements could enhance results. Future numeracy research could add objective numeracy items and assess whether this influences participants' self-perceived numeracy. Based on this sample population of accounting students, the findings may not apply to large populations of accounting-information users.

Investors' ability to exercise sound judgement depends on the accuracy of their probability estimations. Manipulating the format of probability expressions can improve probability estimation performance in investors with low self-evaluated numeracy.

This study identified a significant performance gap among participants in performing accounting probability estimations: those with high self-evaluated numeracy performed better than those with low self-evaluated numeracy. The authors also explored a method other than additional training to improve participants' performance on probability estimation tasks and discovered that frequency formats enhanced the performance of participants with low self-evaluated numeracy.

This article aims to develop procedures for estimation and prediction in case of Type-I hybrid censored samples drawn from a two-parameter generalized half-logistic distribution (GHLD).

The GHLD is a versatile model which is useful in lifetime modelling. Also, hybrid censoring is a time and cost-effective censoring scheme which is widely used in the literature. The authors derive the maximum likelihood estimates, the maximum product of spacing estimates and Bayes estimates with squared error loss function for the unknown parameters, reliability function and stress-strength reliability. The Bayesian estimation is performed under an informative prior set-up using the “importance sampling technique”. Afterwards, we discuss the Bayesian prediction problem under one and two-sample frameworks and obtain the predictive estimates and intervals with corresponding average interval lengths. Applications of the developed theory are illustrated with the help of two real data sets.

The performances of these estimates and prediction methods are examined under Type-I hybrid censoring scheme with different combinations of sample sizes and time points using Monte Carlo simulation techniques. The simulation results show that the developed estimates are quite satisfactory. Bayes estimates and predictive intervals estimate the reliability characteristics efficiently.

The proposed methodology may be used to estimate future observations when the available data are Type-I hybrid censored. This study would help in estimating and predicting the mission time as well as stress-strength reliability when the data are censored.

This paper aims to formulate a hedonic pricing model for Japanese rice wine, sake, via hierarchical Bayesian modeling estimated using an efficient Markov chain Monte Carlo (MCMC) method. Using the estimated model, the authors examine how producing regions, rice breeds and taste characteristics affect sake prices.

The datasets in the estimation consist of cross-sectional observations of 403 sake brands, which include sake prices, taste indicators, premium categories, rice breeds and regional dummy variables. Data were retrieved from Rakuten, Japan’s largest online shopping site. The authors used the Bayesian estimation of the hedonic pricing model and used an ancillarity–sufficiency interweaving strategy to improve the sampling efficiency of MCMC.

The estimation results indicate that Japanese consumers value sweeter sake more, and the price of sake reflects the cost of rice preprocessing only for the most-expensive category of sake. No distinctive differences were identified among rice breeds or producing regions in the hedonic pricing model.

To the best of the authors’ knowledge, this study is the first to estimate a hedonic pricing model of sake, despite the rich literature on alcoholic beverages. The findings may contribute new insights into consumer preference and proper pricing for sake breweries and distributors venturing into the e-commerce market.

One of the crucial issues in the contemporary finance is the prediction of the volatility of financial assets. In this paper, the authors are interested in modelling the stochastic volatility of the MAD/EURO and MAD/USD exchange rates.

For this purpose, the authors have adopted Bayesian approach based on the MCMC (Monte Carlo Markov Chain) algorithm which permits to reproduce the main stylized empirical facts of the assets studied. The data used in this study are the daily historical series of MAD/EURO and MAD/USD exchange rates covering the period from February 2, 2000, to March 3, 2017, which represent 4,456 observations.

By the aid of this approach, the authors were able to estimate all the random parameters of the stochastic volatility model which permit the prediction of the future exchange rates. The authors also have simulated the histograms, the posterior densities as well as the cumulative averages of the model parameters. The predictive efficiency of the stochastic volatility model for Morocco is capable to facilitate the management of the exchange rate in more flexible exchange regime to ensure better targeting of monetary and exchange policies.

To the best of the authors’ knowledge, the novelty of the paper lies in the production of a tool for predicting the evolution of the Moroccan exchange rate and also the design of a tool for the monetary authorities who are today in a proactive conception of management of the rate of exchange. Cyclical policies such as monetary policy and exchange rate policy will introduce this type of modelling into the decision-making process to achieve a better stabilization of the macroeconomic and financial framework.

The purpose of the study is to investigate the impact of implementing anti-dumping duties on imported Australian wine to China in the short- and long-run, respectively.

First, the Difference-in-Differences (DID) method is used in this study to evaluate the short-run causal effect of implementing anti-dumping duties on imported Australian wine to China. Second, a Bayesian ensemble method is used to predict 2023–2025 wine exports from Australia to China. The disparity between the forecasts and counterfactual prediction which assumes no anti-dumping duties represents the accumulated impact of the anti-dumping duties in the long run.

The anti-dumping duties resulted in a significant decline in red and rose, white and sparkling wine exports to China by 92.59%, 99.06% and 90.06%, respectively, in 2021. In the long run, wine exports to China are projected to continue this downward trend, with an average annual growth rate of −21.92%, −38.90% and −9.54% for the three types of wine, respectively. In contrast, the counterfactual prediction indicates an increase of 3.20%, 20.37% and 4.55% for the respective categories. Consequently, the policy intervention is expected to result in a decrease of 96.11%, 93.15% and 84.11% in red and rose, white and sparkling wine exports to China from 2021 to 2025.

The originality of this study lies in the creation of an economic paradigm for assessing policy impacts within the realm of wine economics. Methodologically, it also represents the pioneering application of the DID and Bayesian ensemble forecasting methods within the field of wine economics.

Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods afford inferences based on small data sets and are especially useful in studies with limited data availability. Bayesian approaches also help incorporate prior knowledge, especially subjective knowledge, into predictions. Considering the increasing difficulty in data acquisition, the application of Bayesian techniques can be hugely beneficial to managers, especially in analysing limited data situations like a study of expert opinion. Another factor constraining the broader application of Bayesian statistics in business was computational power requirements and the availability of appropriate analytical tools. However, with the increase in computational power, connectivity and the development of appropriate software programmes, Bayesian applications have become more attractive. This chapter attempts to unravel the applications of the Bayesian inferential procedure in marketing management.