Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results. We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland. Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results. Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.

Bayesian Model Averaging in the Instrumental Variable Regression Model

This paper considers the instrumental variable regression model when there is uncertainty about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainty can result in a huge number of models. To avoid statistical problems associated with standard model selection procedures, we develop a reversible jump Markov chain Monte Carlo algorithm that allows us to do Bayesian model averaging. The algorithm is very exible and can be easily adapted to analyze any of the di¤erent priors that have been proposed in the Bayesian instrumental variables literature. We show how to calculate the probability of any relevant restriction (e.g. the posterior probability that over-identifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returns-to-schooling application.

Reversible behavioural autistic-like regression: A manifestation of a special (new?) epileptic syndrome in a 28-month-old child. A 2-year longitudinal study .

A 28-month-old boy was referred for acute onset of abnormal head movements. History revealed an insidious progressive regression in behaviour and communication over several months. Head and shoulder 'spasms' with alteration of consciousness and on one occasion ictal laughter were seen. The electroencephalograph (EEG) showed repeated bursts of brief generalized polyspikes and spike-wave during the 'spasms', followed by flattening, a special pattern which never recurred after treatment. Review of family videos showed a single 'minor' identical seizure 6 months previously. Magnetic resonance imaging was normal. Clonazepam brought immediate cessation of seizures, normalization of the EEG and a parallel spectacular improvement in communication, mood and language. Follow-up over the next 10 months showed a new regression unaccompained by recognized seizures, although numerous seizures were discovered during the videotaped neuropsychological examination, when stereotyped subtle brief paroxysmal changes in posture and behaviour could be studied in slow motion and compared with the 'prototypical' initial ones. The EEG showed predominant rare left-sided fronto-temporal discharges. Clonazepam was changed to carbamazepin with marked improvement in behaviour, language and cognition which has been sustained up to the last control at 51 months. Videotaped home observations allowed the documentation of striking qualitative and quantitative variations in social interaction and play of autistic type in relation to the epileptic activity. We conclude that this child has a special characteristic epileptic syndrome with subtle motor and vegetative symptomatology associated with an insidious catastrophic 'autistic-like' regression which could be overlooked. The methods used to document such fluctuating epileptic behavioural manifestations are discussed.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

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

Risk factors for head and neck cancer in young adults : a pooled analysis in the INHANCE consortium

BACKGROUND: Increasing incidence of head and neck cancer (HNC) in young adults has been reported. We aimed to compare the role of major risk factors and family history of cancer in HNC in young adults and older patients. METHODS: We pooled data from 25 case-control studies and conducted separate analyses for adults ≤45 years old ('young adults', 2010 cases and 4042 controls) and >45 years old ('older adults', 17 700 cases and 22 704 controls). Using logistic regression with studies treated as random effects, we estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: The young group of cases had a higher proportion of oral tongue cancer (16.0% in women; 11.0% in men) and unspecified oral cavity / oropharynx cancer (16.2%; 11.1%) and a lower proportion of larynx cancer (12.1%; 16.6%) than older adult cases. The proportions of never smokers or never drinkers among female cases were higher than among male cases in both age groups. Positive associations with HNC and duration or pack-years of smoking and drinking were similar across age groups. However, the attributable fractions (AFs) for smoking and drinking were lower in young when compared with older adults (AFs for smoking in young women, older women, young men and older men, respectively, = 19.9% (95% CI = 9.8%, 27.9%), 48.9% (46.6%, 50.8%), 46.2% (38.5%, 52.5%), 64.3% (62.2%, 66.4%); AFs for drinking = 5.3% (-11.2%, 18.0%), 20.0% (14.5%, 25.0%), 21.5% (5.0%, 34.9%) and 50.4% (46.1%, 54.3%). A family history of early-onset cancer was associated with HNC risk in the young [OR = 2.27 (95% CI = 1.26, 4.10)], but not in the older adults [OR = 1.10 (0.91, 1.31)]. The attributable fraction for family history of early-onset cancer was 23.2% (8.60% to 31.4%) in young compared with 2.20% (-2.41%, 5.80%) in older adults. CONCLUSIONS: Differences in HNC aetiology according to age group may exist. The lower AF of cigarette smoking and alcohol drinking in young adults may be due to the reduced length of exposure due to the lower age. Other characteristics, such as those that are inherited, may play a more important role in HNC in young adults compared with older adults.

Prognostic importance of anaemia in patients with acute pulmonary embolism.

Although associated with adverse outcomes in other cardiopulmonary diseases, limited evidence exists on the prognostic value of anaemia in patients with acute pulmonary embolism (PE). We sought to examine the associations between anaemia and mortality and length of hospital stay in patients with PE. We evaluated 14,276 patients with a primary diagnosis of PE from 186 hospitals in Pennsylvania, USA. We used random-intercept logistic regression to assess the association between anaemia at the time of presentation and 30-day mortality and discrete-time logistic hazard models to assess the association between anaemia and time to hospital discharge, adjusting for patient (age, gender, race, insurance type, clinical and laboratory variables) and hospital (region, size, teaching status) factors. Anaemia was present in 38.7% of patients at admission. Patients with anaemia had a higher 30-day mortality (13.7% vs. 6.3%; p <0.001) and a longer length of stay (geometric mean, 6.9 vs. 6.6 days; p <0.001) compared to patients without anaemia. In multivariable analyses, anaemia remained associated with an increased odds of death (OR 1.82, 95% CI: 1.60-2.06) and a decreased odds of discharge (OR 0.85, 95% CI: 0.82-0.89). Anaemia is very common in patients presenting with PE and is independently associated with an increased short-term mortality and length of stay.

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.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

The effectiveness of brief alcohol interventions in primary care settings: a systematic review.

Issues. Numerous studies have reported that brief interventions delivered in primary care are effective in reducing excessive drinking. However, much of this work has been criticised for being clinically unrepresentative. This review aimed to assess the effectiveness of brief interventions in primary care and determine if outcomes differ between efficacy and effectiveness trials. Approach. A pre-specified search strategy was used to search all relevant electronic databases up to 2006. We also hand-searched the reference lists of key articles and reviews. We included randomised controlled trials (RCT) involving patients in primary care who were not seeking alcohol treatment and who received brief intervention. Two authors independently abstracted data and assessed trial quality. Random effects meta-analyses, subgroup and sensitivity analyses and meta-regression were conducted. Key Findings. The primary meta-analysis included 22 RCT and evaluated outcomes in over 5800 patients. At 1 year follow up, patients receiving brief intervention had a significant reduction in alcohol consumption compared with controls [mean difference: -38 g week(-1), 95%CI (confidence interval): -54 to -23], although there was substantial heterogeneity between trials (I(2) = 57%). Subgroup analysis confirmed the benefit of brief intervention in men but not in women. Extended intervention was associated with a non-significantly increased reduction in alcohol consumption compared with brief intervention. There was no significant difference in effect sizes for efficacy and effectiveness trials. Conclusions. Brief interventions can reduce alcohol consumption in men, with benefit at a year after intervention, but they are unproven in women for whom there is insufficient research data. Longer counselling has little additional effect over brief intervention. The lack of differences in outcomes between efficacy and effectiveness trials suggests that the current literature is relevant to routine primary care.

Spectral analysis of random sparse matrices

"Vegeu el resum a l'inici del document del fitxer adjunt."

Propagation connectivity of random hypergraphs

We study the concept of propagation connectivity on random 3-uniform hypergraphs. This concept is inspired by a simple linear time algorithm for solving instances of certain constraint satisfaction problems. We derive upper and lower bounds for the propagation connectivity threshold, and point out some algorithmic implications.

Morphological aspects of the myocarditis and myositis in Calomys callosus experimentally infected with Trypanosoma cruzi: fibrogenesis and spontaneous regression of fibrosis

Calomys callosus a wild rodent, is a natural host of Trypanosoma cruzi. Twelve C. callosus were infected with 10(5) trypomastigotes of the F strain (a myotropic strain) of T. cruzi. Parasitemia decreased on the 21 st day becoming negative around the 40th day of infection. All animals survived but had positive parasitological tests, until the end of the experiment. The infected animals developed severe inflammation in the myocardium and skeletal muscle. This process was pronounced from the 26 th to the 30th day and gradually subsided from the 50 th day becoming absent or residual on the 64 th day after infection. Collagen was identified by the picro Sirius red method. Fibrogenesis developed early, but regression of fibrosis occurred between the 50th and 64th day. Ultrastructural study disclosed a predominance of macrophages and fibroblasts in the inflammatory infiltrates, with small numbers of lymphocytes. Macrophages had active phagocytosis and showed points of contact with altered muscle cells. Different degrees of matrix expansion were present, with granular and fibrilar deposits and collagen bundles. These alterations subsided by the 64th days. Macrophages seem to be the main immune effector cell in the C. callosus model of infection with T. cruzi. The mechanisms involved in the rapid fibrogenesis and its regression deserve further investigation.