Integration of geometric consistency contributory factors in three-leg junctions collision prediction models of Portuguese two-lane national highways

This paper aims at developing a collision prediction model for three-leg junctions located in national roads (NR) in Northern Portugal. The focus is to identify factors that contribute for collision type crashes in those locations, mainly factors related to road geometric consistency, since literature is scarce on those, and to research the impact of three modeling methods: generalized estimating equations, random-effects negative binomial models and random-parameters negative binomial models, on the factors of those models. The database used included data published between 2008 and 2010 of 177 three-leg junctions. It was split in three groups of contributing factors which were tested sequentially for each of the adopted models: at first only traffic, then, traffic and the geometric characteristics of the junctions within their area of influence; and, lastly, factors which show the difference between the geometric characteristics of the segments boarding the junctionsâ area of influence and the segment included in that area were added. The choice of the best modeling technique was supported by the result of a cross validation made to ascertain the best model for the three sets of researched contributing factors. The models fitted with random-parameters negative binomial models had the best performance in the process. In the best models obtained for every modeling technique, the characteristics of the road environment, including proxy measures for the geometric consistency, along with traffic volume, contribute significantly to the number of collisions. Both the variables concerning junctions and the various national highway segments in their area of influence, as well as variations from those characteristics concerning roadway segments which border the already mentioned area of influence have proven their relevance and, therefore, there is a rightful need to incorporate the effect of geometric consistency in the three-leg junctions safety studies.

Power series generalized nonlinear models

We introduce in this paper a new class of discrete generalized nonlinear models to extend the binomial, Poisson and negative binomial models to cope with count data. This class of models includes some important models such as log-nonlinear models, logit, probit and negative binomial nonlinear models, generalized Poisson and generalized negative binomial regression models, among other models, which enables the fitting of a wide range of models to count data. We derive an iterative process for fitting these models by maximum likelihood and discuss inference on the parameters. The usefulness of the new class of models is illustrated with an application to a real data set. (C) 2008 Elsevier B.V. All rights reserved.

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

Self-exciting threshold binomial autoregressive processes

We introduce a new class of integer-valued self-exciting threshold models, which is based on the binomial autoregressive model of order one as introduced by McKenzie (Water Resour Bull 21:645–650, 1985. doi:10.1111/j.1752-1688.1985. tb05379.x). Basic probabilistic and statistical properties of this class of models are discussed. Moreover, parameter estimation and forecasting are addressed. Finally, the performance of these models is illustrated through a simulation study and an empirical application to a set of measle cases in Germany.

Likelihood-based approaches to modeling demand for medical care

We review recent likelihood-based approaches to modeling demand for medical care. A semi-nonparametric model along the lines of Cameron and Johansson's Poisson polynomial model, but using a negative binomial baseline model, is introduced. We apply these models, as well a semiparametric Poisson, hurdle semiparametric Poisson, and finite mixtures of negative binomial models to six measures of health care usage taken from the Medical Expenditure Panel survey. We conclude that most of the models lead to statistically similar results, both in terms of information criteria and conditional and unconditional prediction. This suggests that applied researchers may not need to be overly concerned with the choice of which of these models they use to analyze data on health care demand.

Evidence on the role of ownership structure on firms' innovative performance

Based on the Knowledge Production Function framework given by Griliches -1979-, we slightly modify it so that the innovative output depends upon a set of factors related to the firm internal characteristics and are influenced by the environment. Specifically, regarding the firm internal determinants the effect of the concentration of the ownership, the composition of the boards of directors and the effect of the nature of the ownership (foreign and public) are analyzed. Additionally, in order to capture the determinants of the environment in which the firm operates other variables concerning the internationalization of market, the agglomeration economies and the regional knowledge externalities are also considered. In order to assess the impact of these determinants on the number of patents and models of use awarded by the firm, the discreteness of the latter variable has to be taken into account. We apply Poisson and Negative Binomial models for a more comprehensive evaluation of the hypothesis in a panel of Spanish manufacturing firms. The results show patenting activity is positively favoured by being located in an environment with a high innovative activity, due to the existence of knowledge spillovers and agglomeration economies.

Real option pricing in mixed-use development projects

The application of real options theory to commercial real estate has developed rapidly during the last 15 Years. In particular, several pricing models have been applied to value real options embedded in development projects. In this study we use a case study of a mixed use development scheme and identify the major implied and explicit real options available to the developer. We offer the perspective of a real market application by exploring different binomial models and the associated methods of estimating the crucial parameter of volatility. We include simple binomial lattices, quadranomial lattices and demonstrate the sensitivity of the results to the choice of inputs and method.

Treatment response and mortality among patients starting antiretroviral therapy with and without Kaposi sarcoma: a cohort study

BACKGROUND Improved survival among HIV-infected individuals on antiretroviral therapy (ART) has focused attention on AIDS-related cancers including Kaposi sarcoma (KS). However, the effect of KS on response to ART is not well-described in Southern Africa. We assessed the effect of KS on survival and immunologic and virologic treatment responses at 6- and 12-months after initiation of ART. METHODS We analyzed prospectively collected data from a cohort of HIV-infected adults initiating ART in South Africa. Differences in mortality between those with and without KS at ART initiation were estimated with Cox proportional hazard models. Log-binomial models were used to assess differences in CD4 count response and HIV virologic suppression within a year of initiating treatment. RESULTS Between January 2001-January 2008, 13,847 HIV-infected adults initiated ART at the study clinics. Those with KS at ART initiation (n = 247, 2%) were similar to those without KS (n = 13600,98%) with respect to age (35 vs. 35yrs), presenting CD4 count (74 vs. 85cells/mm³) and proportion on TB treatment (37% vs. 30%). In models adjusted for sex, baseline CD4 count, age, treatment site, tuberculosis and year of ART initiation, KS patients were over three times more likely to have died at any time after ART initiation (hazard ratio[HR]: 3.62; 95% CI: 2.71-4.84) than those without KS. The increased risk was highest within the first year on ART (HR: 4.05; 95% CI: 2.95-5.55) and attenuated thereafter (HR: 2.30; 95% CI: 1.08-4.89). Those with KS also gained, on average, 29 fewer CD4 cells (95% CI: 7-52cells/mm³) and were less likely to increase their CD4 count by 50 cells from baseline (RR: 1.43; 95% CI: 0.99-2.06) within the first 6-months of treatment. CONCLUSIONS HIV-infected adults presenting with KS have increased risk of mortality even after initiation of ART with the greatest risk in the first year. Among those who survive the first year on therapy, subjects with KS demonstrated a poorer immunologic response to ART than those without KS.

Quantal release at a neuronal nicotinic synapse from rat adrenal gland.

The neuronal nicotinic synapse in tissue slices of the adrenal medulla was studied with whole-cell patch-clamp. Excitatory postsynaptic currents (EPSCs) were evoked by local field stimulation or occurred spontaneously especially when external [K+] was increased. EPSCs were carried by channels sharing biophysical and pharmacological properties of neuronal-type nicotinic receptors (nAChRs). A single-channel conductance (gamma) of 43-45 pS was found from nonstationary variance analysis of EPSCs. Spontaneous EPSCs were tetrodotoxin-insensitive and Ca(2+)-dependent and occurred in burst-like clusters. Quantal analysis of spontaneous EPSCs gave a quantal size of 20 pA and amplitude histograms were well described by binomial models with low values of quantal content, consistent with a small number of spontaneously active release sites. However, rare large amplitude EPSCs suggest that the total number of sites is higher and that extrajunctional receptors are involved. Our estimates of quantal content and size at the chromaffin cell neuronal nicotinic synapse may be useful in characterizing central neuronal-type nicotinic receptor-mediated cholinergic synaptic transmission.

Análise genética da produção in vitro de embriões em bovinos Guzerá

O objetivo dessa pesquisa foi avaliar aspectos genéticos que relacionados à produção in vitro de embriões na raça Guzerá. O primeiro estudo focou na estimação de (co) variâncias genéticas e fenotípicas em características relacionadas a produção de embriões e na detecção de possível associação com a idade ao primeiro parto (AFC). Foi detectada baixa e média herdabilidade para características relacionadas à produção de oócitos e embriões. Houve fraca associação genética entre características ligadas a reprodução artificial e a idade ao primeiro parto. O segundo estudo avaliou tendências genéticas e de endogamia em uma população Guzerá no Brasil. Doadoras e embriões produzidos in vitro foram considerados como duas subpopulações de forma a realizar comparações acerca das diferenças de variação anual genética e do coeficiente de endogamia. A tendência anual do coeficiente de endogamia (F) foi superior para a população geral, sendo detectado efeito quadrático. No entanto, a média de F para a sub- população de embriões foi maior do que na população geral e das doadoras. Foi observado ganho genético anual superior para a idade ao primeiro parto e para a produção de leite (305 dias) entre embriões produzidos in vitro do que entre doadoras ou entre a população geral. O terceiro estudo examinou os efeitos do coeficiente de endogamia da doadora, do reprodutor (usado na fertilização in vitro) e dos embriões sobre resultados de produção in vitro de embriões na raça Guzerá. Foi detectado efeito da endogamia da doadora e dos embriões sobre as características estudadas. O quarto (e último) estudo foi elaborado para comparar a adequação de modelos mistos lineares e generalizados sob método de Máxima Verossimilhança Restrita (REML) e sua adequação a variáveis discretas. Quatro modelos hierárquicos assumindo diferentes distribuições para dados de contagem encontrados no banco. Inferência foi realizada com base em diagnósticos de resíduo e comparação de razões entre componentes de variância para os modelos em cada variável. Modelos Poisson superaram tanto o modelo linear (com e sem transformação da variável) quanto binomial negativo à qualidade do ajuste e capacidade preditiva, apesar de claras diferenças observadas na distribuição das variáveis. Entre os modelos testados, a pior qualidade de ajuste foi obtida para o modelo linear mediante transformação logarítmica (Log10 X +1) da variável resposta.

On estimation and influence diagnostics for zero-inflated negative binomial regression models

The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved

Beta-binomial/Poisson regression models for repeated bivariate counts

We analyze data obtained from a study designed to evaluate training effects on the performance of certain motor activities of Parkinson`s disease patients. Maximum likelihood methods were used to fit beta-binomial/Poisson regression models tailored to evaluate the effects of training on the numbers of attempted and successful specified manual movements in 1 min periods, controlling for disease stage and use of the preferred hand. We extend models previously considered by other authors in univariate settings to account for the repeated measures nature of the data. The results suggest that the expected number of attempts and successes increase with training, except for patients with advanced stages of the disease using the non-preferred hand. Copyright (c) 2008 John Wiley & Sons, Ltd.

Are niche-based species distribution models transferable in space?

Aim To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques. Location Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Methods Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions. Results The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3-0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusions The large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche-based models for assessing the fate of species in future environments.

Mixture models for capture-recapture count data

The contribution investigates the problem of estimating the size of a population, also known as the missing cases problem. Suppose a registration system is targeting to identify all cases having a certain characteristic such as a specific disease (cancer, heart disease, ...), disease related condition (HIV, heroin use, ...) or a specific behavior (driving a car without license). Every case in such a registration system has a certain notification history in that it might have been identified several times (at least once) which can be understood as a particular capture-recapture situation. Typically, cases are left out which have never been listed at any occasion, and it is this frequency one wants to estimate. In this paper modelling is concentrating on the counting distribution, e.g. the distribution of the variable that counts how often a given case has been identified by the registration system. Besides very simple models like the binomial or Poisson distribution, finite (nonparametric) mixtures of these are considered providing rather flexible modelling tools. Estimation is done using maximum likelihood by means of the EM algorithm. A case study on heroin users in Bangkok in the year 2001 is completing the contribution.

American option valuation: implied calibration of GARCH pricing models

This study analyzes the issue of American option valuation when the underlying exhibits a GARCH-type volatility process. We propose the usage of Rubinstein's Edgeworth binomial tree (EBT) in contrast to simulation-based methods being considered in previous studies. The EBT-based valuation approach makes an implied calibration of the pricing model feasible. By empirically analyzing the pricing performance of American index and equity options, we illustrate the superiority of the proposed approach.