Browsing by Subject "Bayesian hierarchical models"

Now showing 1 - 3 of 3

Open Access
Employee turnover probability prediction
(2022-09) Barın, Hüsameddin Deniz
Employee turnover prediction is crucial for the companies in the sense that the precautionary action by the employers can be made in advance. A turnover data provided by a company was examined throughout the thesis. Firstly, the missing data were imputed. Then a hierarchical model aiming to explain the attrition heterogeneity among the employees and preventing separation was ﬁtted to the data set. Finally, the results of the implementation were analyzed along with the benchmark models. Based on the results, the proposed hierarchical model had a higher performance on the target metric and the heterogeneity across the units was inferred through the hierarchical model which outperformed the benchmark models.
Open Access
Learning price promotion effects on recurring sell-in purchases from simulated store level sales data
(2021-06) Keşrit, Pelin
When a product is put on promotion to increase its sales, this causes a decrease in the sales of another product in the same product group. This phenomenon happens usually when the promoted product is a substitute for the other product. In this study, we focus on the wholesaler’s revenue maximization problem over the given planning horizon. For this purpose, we constructed a Bayesian hierarchical model for the order quantities observed in the store level data for substitutable products. Order quantities are assumed to have Poisson distributions whose means depend on season, prices and previously ordered quantities for all products in the same group. The customers are assumed to have diﬀerent price sensitivities, and consumption rates implicit in their historical order quantities. Using a hybrid of diﬀerent Markov Chain Monte Carlo methods, we update model parameter posterior distributions and predict each retailer’s order quantities in the future. We veriﬁed on simulated sales data that the MCMC methods work.
Open Access
Production line calibration with data analysis
(2022-09) Taş, İsmail Burak
Product weights can be statistically related to controllable and uncontrollable factors of the production processes. Uncontrollable factors may be correlated with controllable factors. We fitted a response surface approximator of product weights and found sub-optimal controllable factors’ values that minimize product weight. Furthermore, we found that the uncertainty of uncontrollable variables and the correlation among them may affect the result of product weight minimization. The company may implement these findings to reduce the cost of production. Also, we formulated a fully Bayesian experimental design problem to minimize product weight tolerance limits and built hierarchical models. Posterior distributions of the hierarchical models’ parameters can be simulated by a Gibbs sampler. However, we conclude that the effectiveness and convergence of the Gibbs sampler may not be robust to candidate design settings while searching over the design space to solve the experimental design problem.