Feasible estimation of generalized linear mixed models (GLMM) with weak dependency between groups

This paper presents a two-step pseudo likelihood estimation technique for generalized linear mixed models with the random effects being correlated between groups. The core idea is to deal with the intractable integrals in the likelihood function by multivariate Taylor's approximation. The accuracy of the estimation technique is assessed in a Monte-Carlo study. An application of it with a binary response variable is presented using a real data set on credit defaults from two Swedish banks. Thanks to the use of two-step estimation technique, the proposed algorithm outperforms conventional pseudo likelihood algorithms in terms of computational time.

The use of capital budgeting and cost of capital estimation methods in Swedish listed companies

In this paper Swedish listed companies’ use of capital budgeting and cost of capital estimation methods in 2005 and 2008 are examined. The relation between company characteristics and choice of methods is investigated and both within-country longitudinal and cross-country comparisons are made. Larger companies seem to have used capital budgeting methods more frequently than smaller companies. When compared to U.S. and continental European companies, Swedish listed companies employed capital budgeting methods less frequently. In 2005 the most common method for establishing the cost of equity was by asking the investors what return they required. By 2008 CAPM was instead the most utilised method, which could indicate greater sophistication. The use of project risk when evaluating investments also seems to have gained in popularity, while the use of company risk declined. Overall, the use of sophisticated capital budgeting and cost of capital estimation methods seem to be rising and the use of less sophisticated methods declining.

Exposure estimation, uncertainty and variability in occupational hygiene retrospective assessment

This thesis reports on a quantitative exposure assessment and on an analysis of the attributes of the data used in the estimations, in particular distinguishing between its uncertainty and variability. A retrospective assessment of exposure to benzene was carried out for a case control study of leukaemia in the Australian petroleum industry. The study used the mean of personal task-based measurements (Base Estimates) in a deterministic algorithm and applied factors to model back to places, times etc for which no exposure measurements were available. Mean daily exposures were estimated, on an individual subject basis, by summing the task-based exposures. These mean exposures were multiplied by the years spent on each job to provide exposure estimates in ppm-years. These were summed to provide a Cumulative Estimate for each subject. Validation was completed for the model and key inputs. Exposures were low, most jobs were below TWA of 5 ppm benzene. Exposures in terminals were generally higher than at refineries. Cumulative Estimates ranged from 0.005 to 50.9 ppm-years, with 84 percent less than 10 ppm-years. Exposure probability distributions were developed for tanker drivers using Monte Carlo simulation of the exposure estimation algorithm. The outcome was a lognormal distribution of exposure for each driver. These provide the basis for alternative risk assessment metrics e.g. the frequency of short but intense exposures which provided only a minimal contribution to the long-term average exposure but may increase risk of leukaemia. The effect of different inputs to the model were examined and their significance assessed using Monte Carlo simulation. The Base Estimates were the most important determinant of exposure in the model. The sources of variability in the measured data were examined, including the effect of having censored data and the between and within-worker variability. The sources of uncertainty in the exposure estimates were analysed and consequential improvements in exposure assessment identified. Monte Carlo sampling was also used to examine the uncertainties and variability associated with the tanker drivers' exposure assessment, to derive an estimate of the range and to put confidence intervals on the daily mean exposures. The identified uncertainty was less than the variability associated with the estimates. The traditional approach to exposure estimation typically derives only point estimates of mean exposure. The approach developed here allows a range of exposure estimates to be made and provides a more flexible and improved basis for risk assessment.

SAS macros for point and interval estimation of area under the receiver operating characteristic curve for non-proportional and proportional hazards Weibull models

Aims and objectives  For prediction of risk of cardiovascular end points using survival models the proportional hazards assumption is often not met. Thus, non-proportional hazards models are more appropriate for developing risk prediction equations in such situations. However, computer program for evaluating the prediction performance of such models has been rarely addressed. We therefore developed SAS macro programs for evaluating the discriminative ability of a non-proportional hazards Weibull model developed by Anderson (1991) and that of a proportional hazards Weibull model using the area under receiver operating characteristic (ROC) curve.

Method  Two SAS macro programs for non-proportional hazards Weibull model using Proc NLIN and Proc NLP respectively and model validation using area under ROC curve (with its confidence limits) were written with SAS IML language. A similar SAS macro for proportional hazards Weibull model was also written.

Results  The computer program was applied to data on coronary heart disease incidence for a Framingham population cohort. The five risk factors considered were current smoking, age, blood pressure, cholesterol and obesity. The predictive ability of the non-proportional hazard Weibull model was slightly higher than that of its proportional hazard counterpart. An advantage of SAS Proc NLP in terms of the example provided here is that it provides significance level for the parameter estimates whereas Proc NLIN does not.

Conclusion  The program is very useful for evaluating the predictive performance of non-proportional and proportional hazards Weibull models.

Assessing risk post intervention for an acute coronary syndrome: a review of the risk assessment tools and their development

Considerable variability in survival rate after an acute myocardial infarction exists and accurate risk stratification is of significant importance. The American College of Cardiology and the American Heart Association has recommended early risk stratification using several clinical risk scoring instruments to identify high risk patients. The aim of this paper is to identify secondary cardiovascular risk scoring instruments that could be utilized at the time of intervention for acute coronary syndromes and compare their psychometric properties as they were developed. A search using Medline, Cumulative Index to Nursing and Allied Health Literature and the Psychology and Behavioral Sciences Collection data-bases identified studies published between January 1990 and January 2010 used to measure risk after intervention for acute coronary syndrome. Four validated secondary risk prediction scoring instruments were identified for comparison.Secondary risk prediction scoring instruments for the acute coronary syndrome patient population are evidence based, valid and reliable. Use of the instruments by cardiac focused clinicians will aid in the determination of treatment strategies, and estimation of short and long term events and mortality.

Estimating the equity risk premium for economies in the Asian region

The Equity Risk Premium (ERP) is widely used in economic and financial analysis, yet it is difficult to find empirical estimates of the ERP that are generally accepted. The paucity of data in Asian economies exacerbates the problems of estimation. This study estimates the ERP for the larger market-orientated Asian economies and compares the estimates with those of the United States. Surprisingly, of the seven economies examined, the ERP of four cannot be statistically differentiated from that of the United States.

A Gradient-Type Method For Real-Time State Estimation Of Water Distribution Networks

Drinking water distribution networks risk exposure to malicious or accidental contamination. Several levels of responses are conceivable. One of them consists to install a sensor network to monitor the system on real time. Once a contamination has been detected, this is also important to take appropriate counter-measures. In the SMaRT-OnlineWDN project, this relies on modeling to predict both hydraulics and water quality. An online model use makes identification of the contaminant source and simulation of the contaminated area possible. The objective of this paper is to present SMaRT-OnlineWDN experience and research results for hydraulic state estimation with sampling frequency of few minutes. A least squares problem with bound constraints is formulated to adjust demand class coefficient to best fit the observed values at a given time. The criterion is a Huber function to limit the influence of outliers. A Tikhonov regularization is introduced for consideration of prior information on the parameter vector. Then the Levenberg-Marquardt algorithm is applied that use derivative information for limiting the number of iterations. Confidence intervals for the state prediction are also given. The results are presented and discussed on real networks in France and Germany.

Essays on regulatory risk issues

Most studies around that try to verify the existence of regulatory risk look mainly at developed countries. Looking at regulatory risk in emerging market regulated sectors is no less important to improving and increasing investment in those markets. This thesis comprises three papers comprising regulatory risk issues. In the first Paper I check whether CAPM betas capture information on regulatory risk by using a two-step procedure. In the first step I run Kalman Filter estimates and then use these estimated betas as inputs in a Random-Effect panel data model. I find evidence of regulatory risk in electricity, telecommunications and all regulated sectors in Brazil. I find further evidence that regulatory changes in the country either do not reduce or even increase the betas of the regulated sectors, going in the opposite direction to the buffering hypothesis as proposed by Peltzman (1976). In the second Paper I check whether CAPM alphas say something about regulatory risk. I investigate a methodology similar to those used by some regulatory agencies around the world like the Brazilian Electricity Regulatory Agency (ANEEL) that incorporates a specific component of regulatory risk in setting tariffs for regulated sectors. I find using SUR estimates negative and significant alphas for all regulated sectors especially the electricity and telecommunications sectors. This runs in the face of theory that predicts alphas that are not statistically different from zero. I suspect that the significant alphas are related to misspecifications in the traditional CAPM that fail to capture true regulatory risk factors. On of the reasons is that CAPM does not consider factors that are proven to have significant effects on asset pricing, such as Fama and French size (ME) and price-to-book value (ME/BE). In the third Paper, I use two additional factors as controls in the estimation of alphas, and the results are similar. Nevertheless, I find evidence that the negative alphas may be the result of the regulated sectors premiums associated with the three Fama and French factors, particularly the market risk premium. When taken together, ME and ME/BE regulated sectors diminish the statistical significance of market factors premiums, especially for the electricity sector. This show how important is the inclusion of these factors, which unfortunately is scarce in emerging markets like Brazil.

Labor supply, risk aversion and economic refrom in China

Economic reform in China has created a small, but fast-growing private sector that has spurred rapid productivity growth. Growth of the private sector is predicated upon continued labor movements away from state-run industries and into private firms. This paper presents a theory of labor market sectoral choice demonstrating that three factors determine private sector labor supply-the difference in wages between the state and private sectors, private sector wage risk and risk aversion. Estimation of the model using survey data provides strong support for the theory. We find that the riskiness of private sector earnings has a greater effect in discouraging workers from taking jobs in private firms than the wage premi um has in attracting workers.

Effects of long-term cyclosporin therapy on gingiva of rats--analysis by stereological and biochemical estimation.

Cyclosporin A (CsA) is used as an immunosuppressive agent and its prominent side effect is the induction of gingival overgrowth, which remains a significant problem. The risk factors appraised include the duration of treatment. However, there are no stereological and biochemical studies exploring the effects of long-term CsA therapy on gingival tissue. The purpose of the present study was to investigate the level of TGF-beta1 in saliva and describe the densities of fibroblasts and collagen fibers in the gingival tissue of rats treated with CsA for long periods. Rats were treated for 60, 120, 180 and 240 days with a daily subcutaneous injection of 10 mg/kg of body weight of CsA. At the end of the experimental periods, saliva was collected for the determination of TGF-beta1 levels. After histological processing, the oral epithelium and the connective tissue area were measured as well as the volume densities of fibroblasts (Vf) and collagen fibers (Vcf). After 60 and 120 days of CsA treatment, there was a significant increase in Vf and Vcf as well as a significant increase in TGF-beta1 levels. After 180 and 240 days, reduction in the gingival overgrowth associated with significant decreases in the level of TGF-beta1, and also decreased Vf and Vcf, were observed. The data presented here suggest that after long-term therapy, a decrease in TGF-beta1 levels occurs, which might contribute to an increase in the proteolytic activity of fibroblasts in the gingiva, favoring the normality of extracellular matrix synthesis.

Different tools for estimating cardiovascular risk in Brazilian postmenopausal women

Objective: To compare estimation of cardiovascular risk using the Framinghan Risk Score (FRS) and the presence of the metabolic syndrome (MetS) in postmenopausal women to prevent primary cardiovascular disease (CVD). Methods: This cross-sectional study included 497 Brazilian women (aged 45 years and amenorrhea >12months). Cardiovascular risk was calculated using the FRS that includes age, total cholesterol, HDL, systolic blood pressure and smoking status. Women showing three or more of the following criteria were diagnosed with MetS: waist circumference (WC) >88cm, blood pressure 130/85mmHg, triglycerides 150mg/dl, HDL<50mg/dl and glucose 100mg/dl. For statistical analysis, the Chi-square, Fisher's exact, and logistic regression (odds ratio-OR) were used. Results: The mean age was 55.3±7.0 years and time since menopause 7.2±5.9 years. Based on FRS, 72.4% of women were classified as low-risk, 16.5% moderate risk and 11.1% a high-risk. MetS was identified in 40% of the women, and 46.2% were considered of moderate risk for CVD, while 84.9% of those without MetS were classified as low-risk (p<0.001). The risk for CVD increased significantly with age at menopause (OR1.10; 95% CI, 1.04-1.17), time since menopause (OR1.13; 95% CI, 1.08-1.18), elevated triglycerides (OR1.03; 95% CI, 1.0-1.10) and presence of MetS (OR1.72; 95% CI 1.48-1.84). Conclusions: By using only FRS to estimate cardiovascular risk, a substantial number of postmenopausal women showing evidence of MetS were not identified, even though women with MetS are at higher risk of CVD. © 2013 Informa UK Ltd.

The relationship between banking market competition and risk-taking: Do size and capitalization matter?

This paper addresses the effects of bank competition on the risk-taking behaviors of banks in 10 Latin American countries between 2003 and 2008. We conduct our empirical approach in two steps. First, we estimate the Boone indicator, which is a measure of competition. We then regress this measure and other explanatory variables on the banking "stability inefficiency" derived simultaneously from the estimation of a stability stochastic frontier. Unlike previous findings, this paper concludes that competition affects risk-taking behavior in a non-linear way as both high and low competition levels enhance financial stability, while we find the opposite effect for average competition. In addition, bank size and capitalization are essential factors in explaining this relationship. On the one hand, the larger a bank is, the more it benefits from competition. On the other hand, a greater capital ratio is advantageous for banks that operate in collusive markets, while capitalization only enhances the stability of larger banks under high and average competition. These results are of extreme importance when considering bank regulations, especially in light of the recent turmoil in the global financial markets. (C) 2012 Elsevier B.V. All rights reserved.

A new long-term lifetime distribution induced by a latent complementary risk framework

In this paper, we proposed a new three-parameter long-term lifetime distribution induced by a latent complementary risk framework with decreasing, increasing and unimodal hazard function, the long-term complementary exponential geometric distribution. The new distribution arises from latent competing risk scenarios, where the lifetime associated scenario, with a particular risk, is not observable, rather we observe only the maximum lifetime value among all risks, and the presence of long-term survival. The properties of the proposed distribution are discussed, including its probability density function and explicit algebraic formulas for its reliability, hazard and quantile functions and order statistics. The parameter estimation is based on the usual maximum-likelihood approach. A simulation study assesses the performance of the estimation procedure. We compare the new distribution with its particular cases, as well as with the long-term Weibull distribution on three real data sets, observing its potential and competitiveness in comparison with some usual long-term lifetime distributions.

Dense disparity map estimation respecting image discontinuities

[EN] We present an energy based approach to estimate a dense disparity map from a set of two weakly calibrated stereoscopic images while preserving its discontinuities resulting from image boundaries. We first derive a simplified expression for the disparity that allows us to estimate it from a stereo pair of images using an energy minimization approach. We assume that the epipolar geometry is known, and we include this information in the energy model. Discontinuities are preserved by means of a regularization term based on the Nagel-Enkelmann operator. We investigate the associated Euler-Lagrange equation of the energy functional, and we approach the solution of the underlying partial differential equation (PDE) using a gradient descent method The resulting parabolic problem has a unique solution. In order to reduce the risk to be trapped within some irrelevant local minima during the iterations, we use a focusing strategy based on a linear scalespace. Experimental results on both synthetic and real images arere presented to illustrate the capabilities of this PDE and scale-space based method.

Indoor and Outdoor Natural Radioactivity in the Vulsini Volcanic District (Central Italy): Estimation of Doses and Radiological Risks

Terrestrial radioactivity for most individual is the major contributor to the total dose and is mostly provided by 238U, 232Th and 40K radionuclides. In particular indoor radioactivity is principally due to 222Rn, a radioactive noble gas descendent of 238U, second cause of lung cancer after cigarettes smoking. Vulsini Volcanic District is a well known quaternary volcanic area located between the northern Latium and southern Tuscany (Central Italy). It is characterized by an high natural radiation background resulting from the high concentrations of 238U, 232Th and 40K in the volcanic products. In this context, subduction-related metasomatic enrichment of incompatible elements in the mantle source coupled with magma differentiation within the upper crust has given rise to U, Th and K enriched melts. Almost every ancient village and town located in this part of Italy has been built with volcanic rocks pertaining to the Vulsini Volcanic District. The radiological risk of living in this area has been estimated considering separately: a. the risk associated with buildings made of volcanic products and built on volcanic rock substrates b. the risk associated to soil characteristics. The former has been evaluated both using direct 222Rn indoor measurements and simulations of “standard rooms” built with the tuffs and lavas from the Vulsini Volcanic District investigated in this work. The latter has been carried out by using in situ measurements of 222Rn activity in the soil gases. A radon risk map for the Bolsena village has been developed using soil radon measurements integrating geological information. Data of airborne radioactivity in ambient aerosol at two elevated stations in Emilia Romagna (North Italy) under the influence of Fukushima plume have been collected, effective doses have been calculated and an extensive comparison between doses associated with artificial and natural sources in different area have been described and discussed.