Performance of balanced two-stage empirical predictors of realized cluster latent values from finite populations: A simulation study

Predictors of random effects are usually based on the popular mixed effects (ME) model developed under the assumption that the sample is obtained from a conceptual infinite population; such predictors are employed even when the actual population is finite. Two alternatives that incorporate the finite nature of the population are obtained from the superpopulation model proposed by Scott and Smith (1969. Estimation in multi-stage surveys. J. Amer. Statist. Assoc. 64, 830-840) or from the finite population mixed model recently proposed by Stanek and Singer (2004. Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 1119-1130). Predictors derived under the latter model with the additional assumptions that all variance components are known and that within-cluster variances are equal have smaller mean squared error (MSE) than the competitors based on either the ME or Scott and Smith`s models. As population variances are rarely known, we propose method of moment estimators to obtain empirical predictors and conduct a simulation study to evaluate their performance. The results suggest that the finite population mixed model empirical predictor is more stable than its competitors since, in terms of MSE, it is either the best or the second best and when second best, its performance lies within acceptable limits. When both cluster and unit intra-class correlation coefficients are very high (e.g., 0.95 or more), the performance of the empirical predictors derived under the three models is similar. (c) 2007 Elsevier B.V. All rights reserved.

Institutional pressures on financial services firms: the role of information systems in regulatory compliance

The financial crisis of 2008 led to new international regulatory controls for the governance, risk and compliance of financial services firms. Information systems play a critical role here as political, functional and social pressures may lead to the deinstitutionalization of existing structures, processes and practices. This research examines how an investment management system is introduced by a leading IT vendor across eight client sites in the post-crisis era. Using institutional theory, it examines changes in working practices occurring at the environmental and organizational levels and the ways in which technological interventions are used to apply disciplinary effects in order to prevent inappropriate behaviors. The results extend the constructs of deinstitutionalization and identify empirical predictors for the deinstitutionalization of compliance and trading practices within financial organizations.

The role of individualism and collectivism as predictors of attributions for unethical work behavior: An empirical examination across two culturally diverse groups

This dissertation addresses how the cultural dimensions of individualism and collectivism affect the attributions people make for unethical behavior at work. The moderating effect of ethnicity is also examined by considering two culturally diverse groups: Hispanics and Anglos. The sample for this study is a group of business graduate students from two universities in the Southeast. A 20-minute survey was distributed to master's degree students at their classroom and later on returned to the researcher. Individualism and collectivism were operationalized as by a set of attitude items, while unethical work behavior was introduced in the form of hypothetical descriptions or scenarios. Data analysis employed multiple group confirmatory factor analysis for both independent and dependent variables, and subsequently multiple group LISREL models, in order to test predictions. Results confirmed the expected link between cultural variables and attribution responses, although the role of independent variables shifted, due to the moderating effect of ethnicity, and to the nuances of each particular situation. ^

The role of individualism and collectivism as predictors of attributions for unethical work behavior : an empirical examination across two culturally diverse groups

This dissertation addresses how the cultural dimensions of individualism and collectivism affect the attributions people make for unethical behavior at work. The moderating effect of ethnicity is also examined by considering two culturally diverse groups: Hispanics and Anglos. The sample for this study is a group of business graduate students from two universities in the Southeast. A 20-minute survey was distributed to master's degree students at their classroom and later on returned to the researcher. Individualism and collectivism were operationalized as by a set of attitude items, while unethical work behavior was introduced in the form of hypothetical descriptions or scenarios. Data analysis employed multiple group confirmatory factor analysis for both independent and dependent variables, and subsequently multiple group LISREL models, in order to test predictions. Results confirmed the expected link between cultural variables and attribution responses, although the role of independent variables shifted, due to the moderating effect of ethnicity, and to the nuances of each particular situation.

An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations

Background: Genome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases. The astonishing amount of generated data and the use of distinct genotyping platforms with variable genomic coverage are still analytical challenges. Imputation algorithms combine directly genotyped markers information with haplotypic structure for the population of interest for the inference of a badly genotyped or missing marker and are considered a near zero cost approach to allow the comparison and combination of data generated in different studies. Several reports stated that imputed markers have an overall acceptable accuracy but no published report has performed a pair wise comparison of imputed and empiric association statistics of a complete set of GWAS markers. Results: In this report we identified a total of 73 imputed markers that yielded a nominally statistically significant association at P < 10(-5) for type 2 Diabetes Mellitus and compared them with results obtained based on empirical allelic frequencies. Interestingly, despite their overall high correlation, association statistics based on imputed frequencies were discordant in 35 of the 73 (47%) associated markers, considerably inflating the type I error rate of imputed markers. We comprehensively tested several quality thresholds, the haplotypic structure underlying imputed markers and the use of flanking markers as predictors of inaccurate association statistics derived from imputed markers. Conclusions: Our results suggest that association statistics from imputed markers showing specific MAF (Minor Allele Frequencies) range, located in weak linkage disequilibrium blocks or strongly deviating from local patterns of association are prone to have inflated false positive association signals. The present study highlights the potential of imputation procedures and proposes simple procedures for selecting the best imputed markers for follow-up genotyping studies.

Perceptions of organizational virtuousness and happiness and predictors of organizational citizenship behaviors

Journal of Business Ethics, Vol. 93 Issue 2, p214-235

Febrifugine derivative antimalarial activity: quantum mechanical predictors

Plasmodium falciparum resistant strain development has encouraged the search for new antimalarial drugs. Febrifugine is a natural substance with high activity against P. falciparum presenting strong emetic property and liver toxicity, which prevent it from being used as a clinical drug. The search for analogues that could have a better clinical performance is a current topic. We aim to investigate the theoretical electronic structure by means of febrifugine derivative family semi-empirical molecular orbital calculations, seeking the electronic indexes that could help the design of new efficient derivatives. The theoretical results show there is a clustering in well-defined ranges of several electronic indexes of the most selective molecules. The model proposed for achieving high selectivity was tested with success.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Worst-case bounds for the logarithmic loss of predictors

We investigate on-line prediction of individual sequences. Given a class of predictors, the goal is to predict as well as the best predictor in the class, where the loss is measured by the self information (logarithmic) loss function. The excess loss (regret) is closely related to the redundancy of the associated lossless universal code. Using Shtarkov's theorem and tools from empirical process theory, we prove a general upper bound on the best possible (minimax) regret. The bound depends on certain metric properties of the class of predictors. We apply the bound to both parametric and nonparametric classes ofpredictors. Finally, we point out a suboptimal behavior of the popular Bayesian weighted average algorithm.

Socially Competent Leadership - predictors, impacts and skilling in engineering

Earlier management studies have found a relationship between managerial qualities and subordinate impacts, but the effect of managers‘ social competence on leader perceptions has not been solidly established. To fill the related research gap, the present work embarks on a quantitative empirical effort to identify predictors of successful leadership. In particular, this study investigates relationships between perceived leader behavior and three selfreport instruments used to measure managerial capability: 1) the WOPI Work Personality Inventory, 2) Raven‘s general intelligence scale, and 3) the Emotive Communication Scale (ECS). This work complements previous research by resorting to both self-reports and other-reports: the results acquired from the managerial sample are compared to subordinate perceptions as measured through the ECS other-report and the WOPI360 multi-source appraisal. The quantitative research is comprised of a sample of 8o superiors and 354 subordinates operating in eight Finnish organizations. The strongest predictive value emerged from the ECS self- and other-reports and certain personality dimensions. In contrast, supervisors‘ logical intelligence did not correlate with leadership perceived as socially competent by subordinates. 16 of the superiors rated as most socially competent by their subordinates were selected for case analysis. Their qualitative narratives evidence the role of life history and post-traumatic growth in developing managerial skills. The results contribute to leadership theory in four ways. First, the ECS self-report devised for this research offers a reliable scale for predicting socially competent leader ability. Second, the work identifies dimensions of personality and emotive skills that can be considered predictors of managerial ability and benefited from in leader recruitment and career planning. Third, the Emotive Communication Model delineated on the basis of the empirical data allows for a systematic design and planning of communication and leadership education. Fourth, this workfurthers understanding of personal growth strategies and the role of life history in leader development and training. Finally, this research advances educational leadership by conceptualizing and operationalizing effective managerial communications. The Emotive Communication Model devised directs the pedagogic attention in engineering to assertion, emotional availability and inspiration skills. The proposed methodology addresses classroom management strategies drawing from problem-based learning, student empowerment, collaborative learning, and so-called socially competent teachership founded on teacher immediacy and perceived caring, all constituting strategies moving away from student compliance and teacher modelling. The ultimate educational objective embraces the development of individual engineers and organizational leaders that not only possess traditional analytical and technical expertise and substantive knowledge but are intelligent also creatively, practically, and socially.

A global empirical system for probabilistic seasonal climate prediction

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

Febrifugine derivative antimalarial activity: quantum mechanical predictors

O desenvolvimento de linhagens resistentes de Plasmodium falciparum tem encorajado a busca por novas drogas antimalariais. A febrifugina é uma substância natural com alta atividade contra o P. falciparum que apresenta propriedade emética e toxicidade para o fígado tal que não permitem o seu uso clínico. A busca por análogos que possam ter uma performance clínica melhor é um tema de pesquisa atual. Nosso objetivo é investigar a estrutura eletrônica teórica de uma família de derivados da febrifugina empregando cálculos semi-empíricos de orbitais moleculares, procurando por índices eletrônicos que possam ajudar a modelar novos derivados mais eficientes. Os resultados teóricos mostram que para as moléculas mais seletivas existe um agrupamento dos valores de determinados índices em intervalos bem definidos. O modelo proposto para se obter alta seletividade foi testado com sucesso.

Predictors of attempted suicide among Swiss patients with alcohol-use disorders

Objective: Suicide attempts are common in patients being treated for alcohol-use disorders (AUDs). However, clinical assessment of suicide risk is difficult. In this Swiss multisite study, we propose a decision tree to facilitate identification of profiles of AUD patients at high risk for suicidal behavior. Method: In this retrospective study, we used a sample of 700 patients (243 female), attending 1 of 12 treatment programs for AUDs in the German-speaking part of Switzerland. Sixty-nine patients who reported a suicide attempt in the 3 months before the index treatment were compared using risk factors with 631 patients without a suicide attempt. Receiver operating characteristic (ROC) analyses were used to identify patients at risk of having had a suicide attempt in the previous 3 months. Results: Consistent with previous empirical findings in AUD patients, a prior history of attempted suicide and severe symptoms of depression and aggression considerably increased the risk of a suicide attempt and, in combination, raised the likelihood of a prior suicide attempt to 52%. In addition, one third of AUD patients who had a history of suicide attempts and previous inpatient psychiatric treatment, or who were male and had previous inpatient psychiatric treatment, also reported a suicide attempt. Conclusions: The empirically supported decision tree helps to identify profiles of suicidal AUD patients in Switzerland and supplements clinicians' judgments in making triage decisions for suicide management.