Approximate composite marginal likelihood inference in spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.     

Psychic distance, psychic distance paradox and behavioral economics: Modeling MNC entry behavior in foreign markets

Assuming that MNCs face a much more complex environment that local enterprizes, the paper begins by discussing what economist Beckerman called psychic distance. After a historical discussion of this concept, I also discuss what O’Gardy and Lane called psychic distance paradox. Then, I argue that these two concepts have a great deal of relevance beyond their original intent of international trade-they are also relevant to FDI and all other formas of international production and exchange. Next, I argue, as I have done in several previous papers, that behavioral economics has a great deal of relevance to FDI and international productive activity; I also argue that behavioral economics can be utilized to describe the concepts of psychic distance and psychic distance paradox. Then, I develop a behavioral economics-based model that can explain the concepts of psychic distance and psychic distance paradox, and their relevance to the modes of entry of MNCs in international markets. In doing so, I argue that FDI and MNC behavior in general need not be explained outside of economics, since, in contrast to neo-classical economics, behavioral economics is capable of capturing the complexities of global markets.     

BAYESIAN PREDICTION FOR SPATIAL GENERALISED LINEAR MIXED MODELS WITH CLOSED SKEW NORMAL LATENT VARIABLES

Spatial generalised linear mixed models are used commonly for modelling non‐Gaussian discrete spatial responses. In these models, the spatial correlation structure of data is modelled by spatial latent variables. Most users are satisfied with using a normal distribution for these variables, but in many applications it is unclear whether or not the normal assumption holds. This assumption is relaxed in the present work, using a closed skew normal distribution for the spatial latent variables, which is more flexible and includes normal and skew normal distributions. The parameter estimates and spatial predictions are calculated using the Markov Chain Monte Carlo method. Finally, the performance of the proposed model is analysed via two simulation studies, followed by a case study in which practical aspects are dealt with. The proposed model appears to give a smaller cross‐validation mean square error of the spatial prediction than the normal prior in modelling the temperature data set.     

George Katona: A founding father of old behavioral economics

Assuming the division of behavioral economics into old and new, the paper begins to argue that old behavioral economics began with the works of two giants – George Katuna and Herbert Simon during the 1950s and early 1960s. The contributors of Herbert Simon are well established, thanks to the popularity of bounded rationality and satisficing, and his being award Noble Prize in economics. However, economists are much less familiar with the contributions of George Katona that can be viewed as the father of behavioral economics. Furthermore, the author argues that Katona was also misunderstood by various economists when he was attempting to create a psychologically based economics that rejected the mechanistic psychology of neoclassical economics and introducing the survey method to economic research that he had been using in his experimental psychology research previously. He also had influenced various economists during their debates in the 1950s without given the credit for. Many historians of behavioral economics limit Katona's contributions to the start of behavioral economics only to his contributions to macroeconomics. However, the paper demonstrates that Katona's behavioral economics included his contributions to macroeconomics (bringing realism to Keynesian consumption function and consumption behavior), micro-economics (business behavior, the rationality assumption, etc.), public finance and economic policy, and his introduction of the survey method. To demonstrate these contributions, the author argues that Katona attempted to bring realism to economic analysis – through psychological concepts – beginning with his early days of research in Germany which coincided with German hyper inflation- and continued whether working at New school for Social Research, Chicago University's Cowles Commission, the U.S. Department of Agriculture, or the University of Michigan's Survey Research Center. The author also argues that Katona's contributions went through stages, depending upon what economic problem persisted at the time, what advertises he was facing, and what institution/organization he was associated with.     

Identification of outlying height and weight data in the Iranian National Health Survey 1990-92

Data on the weights and heights of children 2-18 yeas old in Iran were obtained in a National Health Survey of 10 660 families in 1990-92. Data were 'cleaned' in 1 year age groups. After excluding gross outliers by inspection of bivariate scatter plots, Box-Cox power transformations were used to normalize the distributions of height and weight. If a multivariate Box-Cox power transformation to normality exists, then it is equivalent to normalizing the data variable by variable. After excluding gross outliers, exclusions based on the Mahalanobis distance were almost identical to those identified by Hadi's iterative procedure, because the percentages of outliers were small. In all, 1% of the observations were gross outliers and a further 0.4% were identified by multivariate analysis. Review of records showed that the outliers identified by multivariate analysis resulted from data-processing errors. After transformation and 'cleaning', the data quality was excellent and suitable for the construction of growth charts.     

An Information Theoretic Framework for Exploratory Multivariate Market Segmentation Research*

State-of-the-art market segmentation often involves simultaneous consideration of multiple and overlapping variables. These variables are studied to assess their relationships, select a subset of variables which best represent the subgroups (segments) within a market, and determine the likelihood of membership of a given individual in a particular segment. Such information, obtained in the exploratory phase of a multivariate market segmentation study, leads to the construction of more parsimonious models. These models have less stringent data requirements while facilitating substantive evaluation to aid marketing managers in formulating more effective targeting and positioning strategies within different market segments. This paper utilizes the information-theoretic (IT) approach to address several issues in multivariate market segmentation studies. A marketing data set analyzed previously is employed to examine the suitability and usefulness of the proposed approach [12]. Some useful extensions of the IT framework and its applications are also discussed.     

Comparative analysis of emergency department patients lost to follow-up after computerized alcohol screening and brief intervention

Many studies have evaluated the effectiveness of alcohol screening and brief intervention (SBI) but most of them have reported substantial loss to follow-up without investigating the characteristics of those lost to follow-up. We examined the association between Alcohol Use Disorders Identification Test (AUDIT) scores, readiness-to-change scores and the demographic factors with lost to follow-up. This retrospective study compared demographic characteristics, AUDIT and readiness-to-change scores for 190 lost to follow-up patients to 221 completed follow-up patients who participated in SBI in the Emergency Department between June 2006 and May 2007. Comparing the association between baseline characteristics and completed follow-up rate, those 30–39, 40–49 and 50 years and older had 0.46 (95% CI 0.32–0.91), 0.49 (95% CI 0.29–0.90) and 0.58 (95%CI 0.22–0.79) lower odds of completing follow-up, respectively, in comparison to those 18–29 years of age. The loss to follow-up group reported more negative consequences of alcohol and binge drinking than the completed follow-up group (p = 0.04). Using logistic regression, patients who experienced more negative effects of alcohol had 0.87 lower odds of completing follow-up (95% CI 0.79–0.96). The patients lost to follow-up in this study were significantly different in age and alcohol drinking habits compared to those completed follow-ups. It is important to consider differential loss to follow-up in assessing the validity and generalizability of intervention studies. This could help in tailoring methods of approaching patients based on target population characteristics.     

An objective look at obtaining the plotting positions for QQ-plots

ABSTRACT

Choosing the plotting positions for the QQ-plot has been a subject of much debate in the statistical and engineering literature. This paper looks at this problem objectively by considering three frameworks: distribution-theoretic; decision-theoretic; game-theoretic. In each framework, we derive the plotting positions and show that there are more than one legitimate solution depending on the practitioner’s objective. This work clarifies the choice of the plotting positions by allowing one to easily find the mathematical equivalent of their view and choose the corresponding solution. This work also discusses approximations to the plotting positions when no closed form is available.     

The Intervening Role of Innovative Climate: A Study of Middle Managers in Manufacturing Organizations in Iran

Based on a developed model, this study deals with an investigation on whether innovative climate is a variable that moderates the relationship among middle managers' technological innovation and organizational factors such as structure, strategic posture, and environment. We used path analysis framework to study this relationship. The respondents (N=158, n=96) were all the managers of large or medium manufacturing companies with headquarters in the province of Tehran in Iran. The study found that innovative climate has an intervening role in the relationship among organizational structure, strategic posture and environment, and middle managers' technological innovation. The results have implications for the executive and human resource management practices.