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The purpose of this paper is to present an economic–statistical design (ESD) for the Bayesian X‾ control chart based on predictive distribution with two types of informative and noninformative prior distributions.

The design used in this study is based on determining the control chart of the predictive distribution and then its ESD. The new proposed cost model is presented by considering the conjugate and Jeffrey's prior distribution in calculating the expected total cycle time and expected cost per cycle, and finally, the optimal design parameters and related costs are compared with the fixed ratio sampling (FRS) mode.

Numerical results show decreases in costs in this Bayesian approach with both Jeffrey's and conjugate prior distribution compared to the FRS mode. This result shows that the Bayesian approach which is based on predictive density works better than the classical approach. Also, for the Bayesian approach, however, there is no significant difference between the results of using Jeffrey's and conjugate prior distributions. Using sensitivity analysis, the effect of cost parameters and shock model parameters and deviation from the mean on the optimal values of design parameters and related costs have been investigated and discussed.

This research adds to the body of knowledge related to quality control of process monitoring systems. This paper may be of particular interest to quality system practitioners for whom the effect of the prior distribution of parameters on the quality characteristic distribution is important.

economic statistical design (ESD) of Bayesian control charts based on predictive distribution is presented for the first time.

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable selection is that we also allow for uncertainty regarding which endogenous variables to include in the model. That is, all models include the forecast variables, but may otherwise have differing sets of endogenous variables. This is a difficult problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the variables of interest and we construct model weights from the predictive likelihood of the forecast variables. The procedure is evaluated in a small simulation study and found to perform competitively in applications to real world data.

The purpose of this paper is to describe a methodology that has been set up to schedule predictive maintenance of distribution transformers at Cauca Department (Colombia) using machine learning.

The proposed methodology relies on classification predictive model that finds the minimal number of distribution transformers prone to failure. To verify this, the model was implemented and tested with real data in Cauca Department Colombia.

The implementation of the methodology allows a saving of 13% in corrective maintenance expenses for the year 2020.

The proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for distribution transformers.

Programs are typically evaluated through the average treatment effect and its standard error. In particular, is the treatment effect positive and is it statistically significant? In theory, programs should be evaluated in a decision framework, using social welfare functions and posterior predictive distributions for outcomes of interest. This chapter discusses the use of stochastic dominance of predictive distributions of outcomes to rank programs, and, under more restrictive parametric and functional form assumptions, the chapter develops intuitive mean-variance tests for program evaluation that are consistent with the underlying decision problem. These concepts are applied to the GAIN and JTPA datasets.

In market share analysis, it is fully recognized that we have often inadmissibly predicted market share, which means that some of predictors take the values outside the range [0, 1] and the total sum of predicted shares is not always one, so‐called “logical inconsistency”. Based on the Bayesian VAR model, proposes a dynamic market share model with logical consistency. The proposed method makes it possible to forecast not only the values of market share themselves, but also various dynamic market share relations across different brands or companies. The daily scanner data from the Nikkei POS information system are analyzed by the proposed method.

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.

Successful strategies for maintenance require good decisions and we commonly use stochastic reliability models to help this process. These models involve unknown parameters, so we gather data to learn about these parameters. However, such data are often difficult to collect for maintenance applications, leading to poor parameter estimates and incorrect decisions. A subjective modelling approach can resolve this problem, but requires us to specify suitable prior distributions for the unknown parameters. This paper considers which priors to adopt for common maintenance models and describes the method of predictive elicitation for determining unknown hyperparameters associated with these prior distributions. We discuss the computational difficulties of this approach and consider numerical methods for resolving this problem. Finally, we present practical demonstrations to illustrate the potential benefits of predictive elicitation and subjective analysis. This work provides a major step forward in making the methods of subjective Bayesian inference available to maintenance decision makers in practice. Practical implications. This paper recommends powerful strategies for expressing subjective knowledge about unknown model parameters, in the context of maintenance applications that involve making decisions.

The purpose of this paper is to bring out the development of a flexible model for nonstationary crop yield distributions and its applications to decision-making in crop insurance.

The authors design a nonparametric Bayesian approach based on Gaussian process regressions to model crop yields over time. Further flexibility is obtained via Bayesian model averaging that results in mixed Gaussian processes.

Simulation results on crop insurance premium rates show that the proposed method compares favorably with conventional estimators, especially when the underlying distributions are nonstationary.

Unlike conventional two-stage estimation, the proposed method models nonstationary crop yields in a single stage. The authors further adopt a decision theoretic framework in its empirical application and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions.