Refik Soyer - Academia.edu (original) (raw)

Papers by Refik Soyer

Springer eBooks, Dec 9, 2021

The American Statistician, Jul 10, 2019

Decision Analysis, Mar 1, 2021

Technometrics, Feb 1, 1995

Applied Stochastic Models in Business and Industry, 2020

Applied Stochastic Models in Business and Industry, 2021

Journal of the Operational Research Society, 2020

Comparison of relative importance of predictors is a subject of discussion of research findings i... more Comparison of relative importance of predictors is a subject of discussion of research findings in many disci- plines, as well as being input for decision-making in business practice. Relative importance methodologists have proposed measures for specific problems such as normal linear regression and logit. Some attempts have been made to set requirements for relative importance of predictors, given a measure of "importance", without characterizing the notion of "importance" itself. The main objective of this paper is to fill this gap by providing a notion of importance of predictors suciently general so as to be applicable to various models and data types, yet to admit a unique interpretation. The importance of predictors is characterized by the ex- tent to which their use reduces uncertainty about predicting the response variable, namely their information importance. Uncertainty associated with a probability distribution is a concave function of the density such...

Production and Operations Management

Nonhomogeneous Poisson process models have commonly been used to analyze and forecast arrivals. S... more Nonhomogeneous Poisson process models have commonly been used to analyze and forecast arrivals. Such processes require specification of intensity (arrival rate) functions, which are typically defined in a parametric form. The accuracy of the parametric models is highly sensitive to the choice of the specific intensity function for the arrival process. We use a Bayesian framework by proposing a nonparametric form for the intensity function and introduce a robust semiparametric model. The model is suitable for analyzing both time of arrival data and interval censored count data and can capture both monotonic and non‐monotonic arrival intensity. The intensity function in the model can be modulated to incorporate auxiliary information as well as seasonal and random effect components. We develop the Bayesian analysis of the proposed model and implement it on two real call center datasets with different characteristics. We also consider several extensions to our model and develop their Ba...

Naval Research Logistics (NRL)

Software debugging is the process of detecting and removing bugs during software development. Alt... more Software debugging is the process of detecting and removing bugs during software development. Although the intent of modifications to the software is to remove bugs, one cannot rule out the possibility of introducing new bugs as a result of these modifications. We consider a self‐exciting point process, which can incorporate the case of reliability deterioration due to the potential introduction of new bugs to the software during the development phase. In order to account for the unobservable process of introducing bugs, latent variables are incorporated into the self‐exciting point process models. The models are then applied to two data sets in software reliability and additional insights that can be obtained from these models are discussed. Our results suggest that the self‐exciting processes with latent factors perform better than the standard point process models in describing the behavior of software failures during the debugging process.

Methodology and Computing in Applied Probability

Probability in the Engineering and Informational Sciences, 2020

Markov processes play an important role in reliability analysis and particularly in modeling the ... more Markov processes play an important role in reliability analysis and particularly in modeling the stochastic evolution of survival/failure behavior of systems. The probability law of Markov processes is described by its generator or the transition rate matrix. In this paper, we suppose that the process is doubly stochastic in the sense that the generator is also stochastic. In our model, we suppose that the entries in the generator change with respect to the changing states of yet another Markov process. This process represents the random environment that the stochastic model operates in. In fact, we have a Markov modulated Markov process which can be modeled as a bivariate Markov process that can be analyzed probabilistically using Markovian analysis. In this setting, however, we are interested in Bayesian inference on model parameters. We present a computationally tractable approach using Gibbs sampling and demonstrate it by numerical illustrations. We also discuss cases that invol...

WIREs Computational Statistics, 2021

In this article, we present an overview of recent advances in Bayesian modeling and analysis of m... more In this article, we present an overview of recent advances in Bayesian modeling and analysis of multivariate time series of counts. We discuss basic modeling strategies including integer valued autoregressive processes, multivariate Poisson time series and dynamic latent factor models. In so doing, we make a connection with univariate modeling frameworks such as dynamic generalized models, Poisson state space models with gamma evolution and present Bayesian approaches that extend these frameworks to multivariate setting. During our development, recent Bayesian approaches to the analysis of integer valued autoregressive processes and multivariate Poisson models are highlighted and concepts such as “decouple/recouple” and “common random environment” are presented. The role that these concepts play in Bayesian modeling and analysis of multivariate time series are discussed. Computational issues associated with Bayesian inference and forecasting from these models are also considered.Thi...

Proceedings of the International Symposium on the Analytic Hierarchy Process, 1994

The paper discusses issues that surround decisions in risk and reliability, with a major emphasis... more The paper discusses issues that surround decisions in risk and reliability, with a major emphasis on quantitative methods. We start with a brief history of quantitative methods in risk and reliability from the 17th century onwards. Then, we look at the principal concepts and methods in decision theory. Finally, we give several examples of their application to a wide variety of risk and reliability problems: software testing, preventive maintenance, portfolio selection, adversarial testing, and the defend-attack problem. These illustrate how the general framework of game and decision theory plays a relevant part in risk and reliability.

Chemical engineering transactions, 2013

The term fraud refers to an intentional deception or misrepresentation made by a person or an ent... more The term fraud refers to an intentional deception or misrepresentation made by a person or an entity, with the knowledge that the deception could result in some kinds of unauthorized benefits to that person or entity. Fraud detection, being part of the overall fraud control, should be automated as much as possible to reduce the manual steps of a screening/checking process. In the health care systems, fraud has led to significant additional expenses. Development of a cost-effective health care system requires effective ways to detect fraud. It is impossible to be certain about the legitimacy of and intention behind an application or transaction. Given the reality, the best cost effective option is to infer potential fraud from the available data using mathematical models and suitable algorithms. Among these, in recent years coclustering has emerged as a powerful data mining tool for analysis of dyadic data connecting two entities. An important data mining task pertinent to dyadic dat...

Journal of Time Series Analysis, 2020

In this article, we propose a class of multivariate non‐Gaussian time series models which include... more In this article, we propose a class of multivariate non‐Gaussian time series models which include dynamic versions of many well‐known distributions and consider their Bayesian analysis. A key feature of our proposed model is its ability to account for correlations across time as well as across series (contemporary) via a common random environment. The proposed modeling approach yields analytically tractable dynamic marginal likelihoods, a property not typically found outside of linear Gaussian time series models. These dynamic marginal likelihoods can be tied back to known static multivariate distributions such as the Lomax, generalized Lomax, and the multivariate Burr distributions. The availability of the marginal likelihoods allows us to develop efficient estimation methods for various settings using Markov chain Monte Carlo as well as sequential Monte Carlo methods. Our approach can be considered to be a multivariate generalization of commonly used univariate non‐Gaussian class ...

Applied Stochastic Models in Business and Industry, 2019

We propose a Bayesian framework to model bid placement time in retail secondary market online bus... more We propose a Bayesian framework to model bid placement time in retail secondary market online business‐to‐business auctions. In doing so, we propose a Bayesian beta regression model to predict the first bidder and time to first bid, and a dynamic probit model to analyze participation. In our development, we consider both auction‐specific and bidder‐specific explanatory variables. While we primarily focus on the predictive performance of the models, we also discuss how auction features and bidders' heterogeneity could affect the bid timings, as well as auction participation. We illustrate the implementation of our models by applying to actual auction data and discuss additional insights provided by the Bayesian approach, which can benefit auctioneers.