Fusion of data sets in multivariate linear regression with errors-in-variables

We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regressionmodels with errors--in--variables, in the case where various data setsare merged into a single analysis and the observable variables deviatepossibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possiblenon--normality of the data, normal--theory methods yield correct inferencesfor the parameters of interest and for the goodness--of--fit test. Thetheory described encompasses both the functional and structural modelcases, and can be implemented using standard software for structuralequations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.

Transition probabilities and duration analysis among disability states: Some evidence from Spanish data

In this paper we study the disability transition probabilities (as well as the mortalityprobabilities) due to concurrent factors to age such as income, gender and education. Althoughit is well known that ageing and socioeconomic status influence the probability ofcausing functional disorders, surprisingly little attention has been paid to the combined effectof those factors along the individuals' life and how this affects the transition from one degreeof disability to another. The assumption that tomorrow's disability state is only a functionof the today's state is very strong, since disability is a complex variable that depends onseveral other elements than time. This paper contributes into the field in two ways: (1) byattending the distinction between the initial disability level and the process that leads tohis course (2) by addressing whether and how education, age and income differentially affectthe disability transitions. Using a Markov chain discrete model and a survival analysis, weestimate the probability by year and individual characteristics that changes the state of disabilityand the duration that it takes its progression in each case. We find that people withan initial state of disability have a higher propensity to change and take less time to transitfrom different stages. Men do that more frequently than women. Education and incomehave negative effects on transition. Moreover, we consider the disability benefits associatedto those changes along different stages of disability and therefore we offer some clues onthe potential savings of preventive actions that may delay or avoid those transitions. Onpure cost considerations, preventive programs for improvement show higher benefits thanthose for preventing deterioration, and in general terms, those focussing individuals below65 should go first. Finally the trend of disability in Spain seems not to change among yearsand regional differences are not found.

New data on the distribution and conservation status of some angiosperms of the Cape Verde Islands, W Africa

New data on the distributibn and conservation status of some angiosperms of the Cape Verde Islands, W Africa Intensive field work aud analysis of the state of biodiversity ou all islands during the years iYY.3 lo IYYY Icd lo lhc publication ol’several contributions lo the flora and vegelalion of the archipclago of Cabo Verde (Brochniann & al. 1997, Gornes & Vera-Cruz 1993. Gonles & al. I9YSa-h. 1998, Games 1997. Kilian & Leyens 1994, Leyens 1998. Leyens & Lobin 1995, Lobin & al. 1995) as well as to the compilation of the First Red Data List for the Cape Verde Islnuds (Lcyrus & Lobin 1996). the elaboration of the National Strategy for Biodiversity Conservation (SIPA 19YY) and ! compilation of all areas in urgent need of protection (Leyens unpubl. diplonla thc.\is IYYJ. Gwnes & al. iu prep.). As part UC the activities of the lnstituto National de Invcstig;u$o c Dcscnvolvitucnto Agriirio (INIDA) and the Dcpurtamcnto de GeociSncias do Institute Supcriot de Educ;u$o t ISE) iutcnsive t’icld studies were conducted PI many diffcrctu localilics OII xcvcr;~I islands. resulting in a thesis tGo~nes IY97) and several terminal study papers (Luz IYYY. Cosi;t 1994. Gonsalvez 1999). The results show that the vegetation and flora of the islands arc still IWI fully known and much more field work is needed. hllhot~gh Sanliiqw is one of lhc islands whcrc lhc firs1 holanicill iIlVcxligilliollx wcrr c:crriul WI (Wcbh 1x49. Schruidt 1x52. Chcvalicr IY35) and where uu~ny intensive field studirs wcrc

New species of Bakeriella (Hymenoptera, Bethylidae) from the New World and new data on geographic distribution

Bakeriella lata sp. nov. (Brazil, Rondônia), Bakeriella aurata sp. nov. (Brazil, Amazonas) and Bakeriella sulcaticeps sp. nov. (Brazil, Amazonas) are described and illustrated. New geographic records and variation data for B. cristata Evans, 1964, B. floridana Evans, 1964, B. flavicornis Kieffer, 1910, B. incompleta Azevedo, 1994, B. mira Evans, 1997, B. montivaga (Kieffer, 1910), B. olmeca Evans, 1964 and B. subcarinata Evans, 1965 are provided. The male of B. incompleta is described for the first time.

Predictability of drug expenditures: An application using morbidity data

The growth of pharmaceutical expenditure and its prediction is a major concern for policy makers and health care managers. This paper explores different predictive models to estimate future drug expenses, using demographic and morbidity individual information from an integrated healthcare delivery organization in Catalonia for years 2002 and 2003. The morbidity information consists of codified health encounters grouped through the Clinical Risk Groups (CRGs). We estimate pharmaceutical costs using several model specifications, and CRGs as risk adjusters, providing an alternative way of obtaining high predictive power comparable to other estimations of drug expenditures in the literature. These results have clear implications for the use of risk adjustment and CRGs in setting the premiums for pharmaceutical benefits.

Bridging DSGE models and the raw data

A method to estimate DSGE models using the raw data is proposed. The approachlinks the observables to the model counterparts via a flexible specification which doesnot require the model-based component to be solely located at business cycle frequencies,allows the non model-based component to take various time series patterns, andpermits model misspecification. Applying standard data transformations induce biasesin structural estimates and distortions in the policy conclusions. The proposed approachrecovers important model-based features in selected experimental designs. Twowidely discussed issues are used to illustrate its practical use.

Crash Data Validation: An Iowa Case Study, February, 2007

With the quickening pace of crash reporting, the statistical editing of data on a weekly basis, and the ability to provide working databases to users at CTRE/Iowa Traffic Safety Data Service, the University of Iowa, and the Iowa DOT, databases that would be considered incomplete by past standards of static data files are in “public use” even as the dynamic nature of the central DOT database allows changes to be made to both the aggregate of data and to the individual crashes already reported. Moreover, “definitive” analyses of serious crashes will, by their nature, lag seriously behind the preliminary data files. Even after these analyses, the dynamic nature of the mainframe data file means that crash numbers can continue to change long after the incident year. The Iowa DOT, its Office of Driver Services (the “data owner”), and institutional data users/distributors must establish data use, distribution, and labeling protocols to deal with the new, dynamic nature of data. In order to set these protocols, data must be collected concerning the magnitude of difference between database records and crash narratives and diagrams. This study determines the difference between database records and crash narratives for the Iowa Department of Transportation’s Office of Traffic and Safety crash database and the impacts of this difference.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.