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A Bayesian approach for misclassified ordinal response data

JOURNAL OF APPLIED STATISTICS 2019年第12期46卷 2198-2215页

作者： Naranjo, Lizbeth Perez, Carlos J. Martin, Jacinto Mutsvari, Timothy Lesaffre, Emmanuel Univ Nacl Autonoma Mexico Fac Ciencias Dept Matemat Mexico City 04510 DF Mexico Univ Extremadura Fac Ciencias Dept Matemat Badajoz Spain Arlenda SA Saint Georges sur Meuse Belgium Katholieke Univ Leuven Sch Publ Hlth L BioStat Leuven Belgium

Motivated by a longitudinal oral health study, the Signal-Tandmobiel (R) study, a Bayesian approach has been developed to model misclassified ordinal response data. Two regression models have been considered to incorporate misclassification in the categorical response. Specifically, probit and logit models have been developed. The computational difficulties have been avoided by using data augmentation. This idea is exploited to derive efficient Markov chain Monte Carlo methods. Although the method is proposed for ordered categories, it can also be implemented for unordered ones in a simple way. The model performance is shown through a simulation-based example and the analysis of the motivating study.

关键词： Bayesian analysis data augmentation Markov chain Monte Carlo methods misclassification ordinal regression model

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modeling Job Satisfaction among Call Center Agents: ordinal regression model

Modeling Job Satisfaction among Call Center Agents: Ordinal ...

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作者： Njoroge Peter Wainaina University of Nairobi

学位级别：硕士

Job satisfaction in call centers offers a multi-disciplinary approach covering such disciplines as Operations Research and Management, Mathematics and Statistics, Forecasting and modeling, Industrial Engineering, Information Technology, Human Resource Management, Psychology, and Sociology. In Mathematics and Statistics context, researchers focuses on efficient call center operation and optimal staffing using mathematical modeling and queuing theory. Call centers are identified by a variety of names: contact center, customer service center, customer interaction center, and call center. A call center is basically a communications link between a company and its customers. It is through the call center that customers give feedback on products and services, the network and suggestions on what can be improved as well as appreciation for the service provided as a company. This study expands the use of statistical models - ordinal regression models to an extensive spectrum of thoughts and links various factors affecting job satisfaction. The researcher investigated the relationship between job satisfaction and factors leading to job dissatisfaction. The study found that there many factors affecting job satisfaction among employee on call centers these are communications within the organization, relationship with coworkers, benefit package which arc equitable, salary increase and supervision in the organization, recognition through promotion, fair payment for the work, reward given to employee and sense of pride in doing my job.

关键词： modelling Job satisfaction Call center agents ordinal regression model Thesis

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Do businesses expect benefits from the existence of metro stations in their area? A case study in Thessaloniki, Greece

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Transportation Research Procedia 2020年 47卷 259-266页

作者： Andreas Nikiforiadis Anastasia Roukouni Socrates Basbas Katerina Chrysostomou Aristotle University of Thessaloniki School of Rural and Surveying Engineering AUTh Campus Thessaloniki 54124 Greece Centre for Research and Technology Hellas Hellenic Institute of Transport 6th km Charilaou – Thermi Rd. Thessaloniki 57001 Greece Delft University of Technology Faculty of Technology Policy and Management Jaffalaan 5 2628 BX Delft the Netherlands

There is a great number of studies which have proved that a new or improved transport system has an important impact on land uses and real estate prices. While there are many studies which examine the effect of a new urban public transport system on the values of neighbouring properties, the number of researches which focus on the identification of the benefits that businesses can expect is rather limited. The objective of the present paper is to identify parameters which have a significant impact on the business revenue, as well as to quantify that impact, focusing on the city of Thessaloniki, Greece, where a metro system is under construction A questionnaire-based survey took place, addressed to business enterprise owners and professionals in the surrounding area of ten of the planned locations of Thessaloniki metro stations. The data collected by this survey were reinforced with additional attributes of the businesses, such as their distance from the nearest metro station, and a statistical analysis has been conducted, utilizing also regression modelling techniques The results indicate that more benefits can be expected for businesses which are located closer to the metro stations, especially in areas with limited parking availability. The benefits are estimated to be higher for the land use category that includes restaurants/café/bars. models of that type can be very useful in cases of implementing alternative funding/financing methods, such as Value Capture. For the implementation of such methods it is very important to accurately estimate the benefits that all the parties involved in an investment will gain.

关键词： business revenue metro system ordinal regression model questionnaire-based survey

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Influential variables in the Journal Impact Factor of Dentistry journals

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HELIYON 2020年第3期6卷 e03575-e03575页

作者： Valderrama, Pilar Escabias, Manuel Valderrama, Mariano J. Jimenez-Contreras, Evaristo Baca, Pilar Univ Granada Vice Rectorate Res & Transfer E-18071 Granada Spain Univ Granada Dept Stat & Operat Res E-18071 Granada Spain Univ Granada Dept Informat & Commun E-18071 Granada Spain Univ Granada Dept Dent E-18071 Granada Spain

Objective: The aim of this contribution is to determine what variables influence the position, by quartiles of the impact factor, as a quality indicator of a journal in the field of Dentistry. Methods: To this end, 24 journals included in Journal Citation Reports, 6 pertaining to each quartile were selected by a stratified sampling and then an ordinal regression model was estimated stepwise considering the journal impact factor quartile as response variable. Results: The estimation procedure concluded that the average number of papers published yearly by a journal and the percentage of systematic reviews are the most significant variables to be considered, along with the factor representing the journal's degree of adherence to recommendations by the International Committee of Medical Journal Editors. Conclusions/Clinical significance: Systematic reviews have significant effect on the Journal Impact Factor position of a journal as well as adherence to ICMJE recommendations, while papers publishing clinical trials bear no influence on this factor. Greater yearly average of published papers in a journal means a higher impact factor.

关键词： Journal impact factor ordinal regression model Dentistry Statistics Applied computing Education Information science

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Can pedestrians and cyclists share the same space? The case of a city with low cycling levels and experience

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SUSTAINABLE CITIES AND SOCIETY 2019年 46卷 101453-000页

作者： Nikiforiadis, Andreas Basbas, Socrates Aristotle Univ Thessaloniki Dept Transportat & Hydraul Engn Sch Rural & Surveying Engn Thessaloniki 54124 Greece

Transport policy objectives, as well as lack of space in the built environment make clear that the coexistence of pedestrians and cyclists is an important issue. For that reason, a survey was conducted on two sidewalks and one pedestrian street in the city of Thessaloniki, Greece where the cycling volumes and cycling experience are still low;however efforts are being made to promote active transport modes (e.g. cycling, walking). In the analysis made in this paper, an ordinal regression model was developed that utilizes questionnaire survey data and field measurements. The aim of the ordinal regression model is to identify and quantify the impact of geometric, functional and social factors on pedestrians' perception about the presence of bicycles and the infrastructure level of service (LOS). The results show a clear difference in the perceptions according to gender and age. Females tend to perceive lower LOS and the same applies for the younger people. Regarding the geometric and functional attributes, pedestrian unit flow rate has been found to affect perceived LOS to a large extent. Another conclusion that emerges from the analysis is the importance of a proper distribution of the available space between pedestrians and cyclists, as over-dimensioning of the bicycle lane has a significant impact on the perceptions of pedestrians, regardless the bicycle volumes.

关键词： Pedestrians Cyclists Shared space Level of service (LOS) ordinal regression model

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Quantifying rater variation for ordinal data using a rating scale model

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STATISTICS IN MEDICINE 2018年第14期37卷 2223-2237页

作者： Zhang, Shiqi Petersen, Jorgen Holm Univ Copenhagen Dept Biostat Oster Farimagsgade 5 Entrance BPostboks 2099 DK-1014 Kbenhavn Denmark

We present a model-based approach to the analysis of agreement between different raters in a situation where all raters have supplied ordinal ratings of the same cases in a sample. It is assumed that no "gold standard" is available. The model is an ordinal regression model with random effects-a so-called rating scale model. The model includes case-specific parameters that allow each case his or hers own level (disease severity). It also allows raters to have different propensities to score a given set of individuals more or less positively-the rater level. Based on the model, we suggest quantifying the rater variation using the median odds ratio. This allows expressing the variation on the same scale as the observed ordinal data. An important example that will serve to motivate and illustrate the proposed model is the study of breast cancer diagnosis based on screening mammograms. The purpose of the assessment is to detect early breast cancer in order to obtain improved cancer survival. In the study, mammograms from 148 women were evaluated by 110 expert radiologists. The experts were asked to rate each mammogram on a 5-point scale ranging from "normal" to "probably malignant."

关键词： breast cancer diagnosis inter-rater variation MOR ordinal regression model rating scale model

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Which Log Level Should Developers Choose for a New Logging Statement? (Journal-First Abstract) 25

Which Log Level Should Developers Choose for a New Logging S...

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25th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Li, Heng Shang, Weiyi Hassan, Ahmed E. Queens Univ Software Anal & Intelligence Lab SAIL Kingston ON Canada Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada

ISBN: (纸本)9781538649695

This is an extended abstract of a paper published in the Empirical Software Engineering journal. The original paper is communicated by Mark Grechanik. The paper empirically studied how developers assign log levels to their logging statements and proposed an automated approach to help developers determine the most appropriate log level when they add a new logging statement. We analyzed the development history of four open source projects (Hadoop, Directory Server, Hama, and Qpid). We found that our automated approach can accurately suggest the levels of logging statements with an AUC of 0.75 to 0.81. We also found that the characteristics of the containing block of a newly-added logging statement, the existing logging statements in the containing source code file, and the content of the newly-added logging statement play important roles in determining the appropriate log level for that logging statement.

关键词： software logging log level ordinal regression model

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Which log level should developers choose for a new logging statement?

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EMPIRICAL SOFTWARE ENGINEERING 2017年第4期22卷 1684-1716页

作者： Li, Heng Shang, Weiyi Hassan, Ahmed E. Queens Univ SAIL Kingston ON Canada Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada

Logging statements are used to record valuable runtime information about applications. Each logging statement is assigned a log level such that users can disable some verbose log messages while allowing the printing of other important ones. However, prior research finds that developers often have difficulties when determining the appropriate level for their logging statements. In this paper, we propose an approach to help developers determine the appropriate log level when they add a new logging statement. We analyze the development history of four open source projects (Hadoop, Directory Server, Hama, and Qpid), and leverage ordinal regression models to automatically suggest the most appropriate level for each newly-added logging statement. First, we find that our ordinal regression model can accurately suggest the levels of logging statements with an AUC (area under the curve;the higher the better) of 0.75 to 0.81 and a Brier score (the lower the better) of 0.44 to 0.66, which is better than randomly guessing the appropriate log level (with an AUC of 0.50 and a Brier score of 0.80 to 0.83) or naively guessing the log level based on the proportional distribution of each log level (with an AUC of 0.50 and a Brier score of 0.65 to 0.76). Second, we find that the characteristics of the containing block of a newly-added logging statement, the existing logging statements in the containing source code file, and the content of the newly-added logging statement play important roles in determining the appropriate log level for that logging statement.

关键词： Logging statement Log level ordinal regression model

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modeling continuous response variables using ordinal regression

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STATISTICS IN MEDICINE 2017年第27期36卷 4316-4335页

作者： Liu, Qi Shepherd, Bryan E. Li, Chun Harrell, Frank E., Jr. Vanderbilt Univ Dept Biostat Nashville TN 37203 USA Case Western Reserve Univ Dept Populat & Quantitat Hlth Sci Cleveland OH 44106 USA

We study the application of a widely used ordinal regression model, the cumulative probability model (CPM), for continuous outcomes. Such models are attractive for the analysis of continuous response variables because they are invariant to any monotonic transformation of the outcome and because they directly model the cumulative distribution function from which summaries such as expectations and quantiles can easily be derived. Such models can also readily handle mixed type distributions. We describe the motivation, estimation, inference, model assumptions, and diagnostics. We demonstrate that CPMs applied to continuous outcomes are semiparametric transformation models. Extensive simulations are performed to investigate the finite sample performance of these models. We find that properly specified CPMs generally have good finite sample performance with moderate sample sizes, but that bias may occur when the sample size is small. Cumulative probability models are fairly robust to minor or moderate link function misspecification in our simulations. For certain purposes, the CPMs are more efficient than other models. We illustrate their application, with model diagnostics, in a study of the treatment of HIV. CD4 cell count and viral load 6months after the initiation of antiretroviral therapy are modeled using CPMs;both variables typically require transformations, and viral load has a large proportion of measurements below a detection limit.

关键词： nonparametric maximum likelihood estimation ordinal regression model rank-based statistics semiparametric transformation model

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Effect of the number of categories, number of time-points, and sample size on the recovery of random-effect ordinal regression model parameters

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JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS 2011年第6期14卷 1175-1198页

作者： Hung, Lai-Fa Huang, Chung-Ming Chang Jung Christian Univ Dept Engn & Management Adv Technol Tainan Taiwan

The random-effect ordinal regression model (REORM) is formulated as a special case of the hierarchical generalized linear model to analyze the effect of a cluster randomized trial on the ordinal responses measured at several occasions. The random-effect model takes the dependency between observations based on the same cluster or subject into account by introducing one or more random effects. The parameters in the REORM can be estimated using the computer program WinBUGS, which adopts Markov chain Monte Carlo (MCMC) algorithms. An analysis of real data is provided as an example. Simulations were conducted to examine parameter recovery based on the best-fitting model in the empirical example. The results show that increasing sample size improved the accuracy of the random-effect variance component recovery. Increasing the number of categories and sample sizes improved the accuracy of threshold recovery, and increasing the number of time-points improved the accuracy of regression parameter recovery.

关键词： random-effect ordinal regression model Markov chain Monte Carlo Gibbs sampler likelihood ratio statistic

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