Social network measures for "nosduocentered" networks, their predictive power on performance

Our purpose in this article is to define a network structure which is based on two egos instead of the egocentered (one ego) or the complete network (n egos). We describe the characteristics and properties for this kind of network which we call “nosduocentered network”, comparing it with complete and egocentered networks. The key point for this kind of network is that relations exist between the two main egos and all alters, but relations among others are not observed. After that, we use new social network measures adapted to the nosduocentered network, some of which are based on measures for complete networks such as degree, betweenness, closeness centrality or density, while some others are tailormade for nosduocentered networks. We specify three regression models to predict research performance of PhD students based on these social network measures for different networks such as advice, collaboration, emotional support and trust. Data used are from Slovenian PhD students and their s

First trimester maternal level of PAPP-A, βhCG and ethnicity as predictive of Intrauterine Growth Restriction

Intrauterine growth restriction (IUGR) is one of the leading causes of perinatal mortality and morbidity. Nowadays, this condition is detected in the 3rt and last trimester of gestation when the pathology is already established and success of therapeutic strategies are limited. As the physiopathology of the disease suggests that the problem stems from poor placental implantation, it would be quite advantageous to identify women at increased risk in the first or second trimester of gestation because it then might be possible to offer treatment interventions or at least to establish increased surveillance for high risk pregnancies. Maternal levels of pregnancy-associated plasma protein-A (PAPP-A) and free β human chorionic gonadotropin (free βhCG) has been shown to be effective in first trimester screening for chromosomal abnormalities, primarily trisomies 21, 13 and 18. Previous studies evaluating PAPP-A and free βhCG measured in the first trimester in relation with IUGR have provided conflicting results. Moreover, it has been suggested that black ethnicity is another important predictive factor for fetal growth restriction.Objective: To analyse the association between first trimester serum analytes (PAPP-A and free βhCG) and ethnicity with Intrauterine Growth Restriction.Methods: The study consists in a retrospective cohort, including all singleton pregnancies with complete outcome data that had undergone first trimester screening (PAPP-A and free βhCG) at 11-13+6weeks of gestation between 1/1/2010 - 31/12/2012 in Hospital Universitari Dr Josep Trueta. Biochemical markers are converted to multiples of the median (MoMs) and percentiles 5 and 10 are calculated. The association between free βhCG and PAPP-A with the incidence of IUGR is evaluated in combination with maternal ethnicity. Bivariate and logistic regression analyses are performed to adjust this association for co variables

First trimester maternal level of PAPP-A, βhCG and ethnicity as predictive of Intrauterine Growth Restriction

Intrauterine growth restriction (IUGR) is one of the leading causes of perinatal mortality and morbidity. Nowadays, this condition is detected in the 3rt and last trimester of gestation when the pathology is already established and success of therapeutic strategies are limited. As the physiopathology of the disease suggests that the problem stems from poor placental implantation, it would be quite advantageous to identify women at increased risk in the first or second trimester of gestation because it then might be possible to offer treatment interventions or at least to establish increased surveillance for high risk pregnancies. Maternal levels of pregnancy-associated plasma protein-A (PAPP-A) and free β human chorionic gonadotropin (free βhCG) has been shown to be effective in first trimester screening for chromosomal abnormalities, primarily trisomies 21, 13 and 18. Previous studies evaluating PAPP-A and free βhCG measured in the first trimester in relation with IUGR have provided conflicting results. Moreover, it has been suggested that black ethnicity is another important predictive factor for fetal growth restriction.Objective: To analyse the association between first trimester serum analytes (PAPP-A and free βhCG) and ethnicity with Intrauterine Growth Restriction.Methods: The study consists in a retrospective cohort, including all singleton pregnancies with complete outcome data that had undergone first trimester screening (PAPP-A and free βhCG) at 11-13+6weeks of gestation between 1/1/2010 - 31/12/2012 in Hospital Universitari Dr Josep Trueta. Biochemical markers are converted to multiples of the median (MoMs) and percentiles 5 and 10 are calculated. The association between free βhCG and PAPP-A with the incidence of IUGR is evaluated in combination with maternal ethnicity. Bivariate and logistic regression analyses are performed to adjust this association for co variables

First trimester maternal level of PAPP-A, βhCG and ethnicity as predictive of Intrauterine Growth Restriction

Intrauterine growth restriction (IUGR) is one of the leading causes of perinatal mortality and morbidity. Nowadays, this condition is detected in the 3rt and last trimester of gestation when the pathology is already established and success of therapeutic strategies are limited. As the physiopathology of the disease suggests that the problem stems from poor placental implantation, it would be quite advantageous to identify women at increased risk in the first or second trimester of gestation because it then might be possible to offer treatment interventions or at least to establish increased surveillance for high risk pregnancies. Maternal levels of pregnancy-associated plasma protein-A (PAPP-A) and free β human chorionic gonadotropin (free βhCG) has been shown to be effective in first trimester screening for chromosomal abnormalities, primarily trisomies 21, 13 and 18. Previous studies evaluating PAPP-A and free βhCG measured in the first trimester in relation with IUGR have provided conflicting results. Moreover, it has been suggested that black ethnicity is another important predictive factor for fetal growth restriction.Objective: To analyse the association between first trimester serum analytes (PAPP-A and free βhCG) and ethnicity with Intrauterine Growth Restriction.Methods: The study consists in a retrospective cohort, including all singleton pregnancies with complete outcome data that had undergone first trimester screening (PAPP-A and free βhCG) at 11-13+6weeks of gestation between 1/1/2010 - 31/12/2012 in Hospital Universitari Dr Josep Trueta. Biochemical markers are converted to multiples of the median (MoMs) and percentiles 5 and 10 are calculated. The association between free βhCG and PAPP-A with the incidence of IUGR is evaluated in combination with maternal ethnicity. Bivariate and logistic regression analyses are performed to adjust this association for co variables

Innovation sources and productivity in Catalonian firms: a quantile regression analysis

This paper explores the effects of two main sources of innovation -intramural and external R&D- on the productivity level in a sample of 3,267 Catalonian firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and knowledge-intensive services. JEL codes: O300, C100, O140. Keywords: Innovation sources, R&D, Productivity, Quantile regression

A priori ratemaking using bivariate poisson regression models

In automobile insurance, it is useful to achieve a priori ratemaking by resorting to gene- ralized linear models, and here the Poisson regression model constitutes the most widely accepted basis. However, insurance companies distinguish between claims with or without bodily injuries, or claims with full or partial liability of the insured driver. This paper exa- mines an a priori ratemaking procedure when including two di®erent types of claim. When assuming independence between claim types, the premium can be obtained by summing the premiums for each type of guarantee and is dependent on the rating factors chosen. If the independence assumption is relaxed, then it is unclear as to how the tari® system might be a®ected. In order to answer this question, bivariate Poisson regression models, suitable for paired count data exhibiting correlation, are introduced. It is shown that the usual independence assumption is unrealistic here. These models are applied to an automobile insurance claims database containing 80,994 contracts belonging to a Spanish insurance company. Finally, the consequences for pure and loaded premiums when the independence assumption is relaxed by using a bivariate Poisson regression model are analysed.

Innovation sources and productivity: a quantile regression analysis

This paper explores the effects of two main sources of innovation —intramural and external R&D— on the productivity level in a sample of 3,267 Catalan firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and services industries.

Factors explaining local privatization: A meta-regression analysis

Privatization of local public services has been implemented worldwide in the last decades. Why local governments privatize has been the subject of much discussion, and many empirical works have been devoted to analyzing the factors that explain local privatization. Such works have found a great diversity of motivations, and the variation among reported empirical results is large. To investigate this diversity we undertake a meta-regression analysis of the factors explaining the decision to privatize local services. Overall, our results indicate that significant relationships are very dependent upon the characteristics of the studies. Indeed, fiscal stress and political considerations have been found to contribute to local privatization specially in the studies of US cases published in the eighties that consider a broad range of services. Studies that focus on one service capture more accurately the influence of scale economies on privatization. Finally, governments of small towns are more affected by fiscal stress, political considerations and economic efficiency, while ideology seems to play a major role for large cities.

Estimation of lean meat content in pig carcasses using X-ray Computed Tomography and PLS regression

Lean meat percentage (LMP) is an important carcass quality parameter. The aim of this work is to obtain a calibration equation for the Computed Tomography (CT) scans with the Partial Least Square Regression (PLS) technique in order to predict the LMP of the carcass and the different cuts and to study and compare two different methodologies of the selection of the variables (Variable Importance for Projection — VIP- and Stepwise) to be included in the prediction equation. The error of prediction with cross-validation (RMSEPCV) of the LMP obtained with PLS and selection based on VIP value was 0.82% and for stepwise selection it was 0.83%. The prediction of the LMP scanning only the ham had a RMSEPCV of 0.97% and if the ham and the loin were scanned the RMSEPCV was 0.90%. Results indicate that for CT data both VIP and stepwise selection are good methods. Moreover the scanning of only the ham allowed us to obtain a good prediction of the LMP of the whole carcass.

Innovation sources and productivity in Catalonian firms: a quantile regression analysis

This paper explores the effects of two main sources of innovation - intramural and external R&D— on the productivity level in a sample of 3,267 Catalonian firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and knowledge-intensive services. JEL codes: O300, C100, O140 Keywords: Innovation sources, R&D, Productivity, Quantile Regression

Modelling dependence in a ratemaking procedure with multivariate Poisson regression models

When actuaries face with the problem of pricing an insurance contract that contains different types of coverage, such as a motor insurance or homeowner's insurance policy, they usually assume that types of claim are independent. However, this assumption may not be realistic: several studies have shown that there is a positive correlation between types of claim. Here we introduce different regression models in order to relax the independence assumption, including zero-inflated models to account for excess of zeros and overdispersion. These models have been largely ignored to multivariate Poisson date, mainly because of their computational di±culties. Bayesian inference based on MCMC helps to solve this problem (and also lets us derive, for several quantities of interest, posterior summaries to account for uncertainty). Finally, these models are applied to an automobile insurance claims database with three different types of claims. We analyse the consequences for pure and loaded premiums when the independence assumption is relaxed by using different multivariate Poisson regression models and their zero-inflated versions.

Predictive and distributed routing balancing (PR-DRB): high speed interconnection networks

Current parallel applications running on clusters require the use of an interconnection network to perform communications among all computing nodes available. Imbalance of communications can produce network congestion, reducing throughput and increasing latency, degrading the overall system performance. On the other hand, parallel applications running on these networks posses representative stages which allow their characterization, as well as repetitive behavior that can be identified on the basis of this characterization. This work presents the Predictive and Distributed Routing Balancing (PR-DRB), a new method developed to gradually control network congestion, based on paths expansion, traffic distribution and effective traffic load, in order to maintain low latency values. PR-DRB monitors messages latencies on intermediate routers, makes decisions about alternative paths and record communication pattern information encountered during congestion situation. Based on the concept of applications repetitiveness, best solution recorded are reapplied when saved communication pattern re-appears. Traffic congestion experiments were conducted in order to evaluate the performance of the method, and improvements were observed.

Mixture of bivariate Poisson regression models with an application to insurance

In a recent paper Bermúdez [2009] used bivariate Poisson regression models for ratemaking in car insurance, and included zero-inflated models to account for the excess of zeros and the overdispersion in the data set. In the present paper, we revisit this model in order to consider alternatives. We propose a 2-finite mixture of bivariate Poisson regression models to demonstrate that the overdispersion in the data requires more structure if it is to be taken into account, and that a simple zero-inflated bivariate Poisson model does not suffice. At the same time, we show that a finite mixture of bivariate Poisson regression models embraces zero-inflated bivariate Poisson regression models as a special case. Additionally, we describe a model in which the mixing proportions are dependent on covariates when modelling the way in which each individual belongs to a separate cluster. Finally, an EM algorithm is provided in order to ensure the models’ ease-of-fit. These models are applied to the same automobile insurance claims data set as used in Bermúdez [2009] and it is shown that the modelling of the data set can be improved considerably.

A logistic regression approach to estimating customer profit loss due to lapses in insurance

This article focuses on business risk management in the insurance industry. A methodology for estimating the profit loss caused by each customer in the portfolio due to policy cancellation is proposed. Using data from a European insurance company, customer behaviour over time is analyzed in order to estimate the probability of policy cancelation and the resulting potential profit loss due to cancellation. Customers may have up to two different lines of business contracts: motor insurance and other diverse insurance (such as, home contents, life or accident insurance). Implications for understanding customer cancellation behaviour as the core of business risk management are outlined.

Time-series regression models to study the short-term effects of environmental factors on health

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants