Economics of public governance with strategic production of information: four essays

SUMMARY This paper analyses the outcomes of the EEA and bilateral agreements vote at the level of the 3025 communities of the Swiss Confederation by simultaneously modelling the vote and the participation decisions. Regressions include economic and political factors. The economic variables are the aggregated shares of people employed in the losing, Winning and neutral sectors, according to BRUNETTI, JAGGI and WEDER (1998) classification, Which follows a Ricardo-Viner logic, and the average education levels, which follows a Heckscher-Ohlin approach. The political factors are those used in the recent literature. The results are extremely precise and consistent. Most of the variables have the predicted sign and are significant at the l % level. More than 80 % of the communities' vote variance is explained by the model, substantially reducing the residuals when compared to former studies. The political variables do have the expected signs and are significant as Well. Our results underline the importance of the interaction between electoral choice and participation decisions as well as the importance of simultaneously dealing with those issues. Eventually they reveal the electorate's high level of information and rationality. ZUSAMMENFASSUNG Unser Beitrag analysiert in einem Model, welches gleichzeitig die Stimm- ("ja" oder "nein") und Partizipationsentscheidung einbezieht, den Ausgang der Abstimmungen über den Beitritt zum EWR und über die bilateralen Verträge für die 3025 Gemeinden der Schweiz. Die Regressionsgleichungen beinhalten ökonomische und politische Variabeln. Die ökonomischen Variabeln beinhalten die Anteile an sektoriellen Arbeitsplatzen, die, wie in BRUNETTI, JAGGIl.1I1d WEDER (1998), in Gewinner, Verlierer und Neutrale aufgeteilt Wurden, gemäß dem Model von Ricardo-Viner, und das durchschnittliche Ausbildungsniveau, gemäß dem Model von Heckscher-Ohlin. Die politischen Variabeln sind die in der gegenwärtigen Literatur üblichen. Unsere Resultate sind bemerkenswert präzise und kohärent. Die meisten Variabeln haben das von der Theorie vorausgesagte Vorzeichen und sind hoch signifikant (l%). Mehr als 80% der Varianz der Stimmabgabe in den Gemeinden wird durch das Modell erklärt, was, im Vergleich mit früheren Arbeiten, die unerklärten Residuen Wesentlich verkleinert. Die politischen Variabeln haben auch die erwarteten Vorzeichen und sind signifikant. Unsere Resultate unterstreichen die Bedeutung der Interaktion zwischen der Stimm- und der Partizipationsentscheidung, und die Bedeutung diese gleichzeitig zu behandeln. Letztendlich, belegen sie den hohen lnformationsgrad und die hohe Rationalität der Stimmbürger. RESUME Le présent article analyse les résultats des votations sur l'EEE et sur les accords bilatéraux au niveau des 3025 communes de la Confédération en modélisant simultanément les décisions de vote ("oui" ou "non") et de participation. Les régressions incluent des déterminants économiques et politiques. Les déterminants économiques sont les parts d'emploi sectoriels agrégées en perdants, gagnants et neutres selon la classification de BRUNETTI, JAGGI ET WEDER (1998), suivant la logique du modèle Ricardo-Viner, et les niveaux de diplômes moyens, suivant celle du modèle Heckscher-Ohlin. Les déterminants politiques suivent de près ceux utilisés dans la littérature récente. Les résultats sont remarquablement précis et cohérents. La plupart des variables ont les signes prédits par les modèles et sont significatives a 1%. Plus de 80% de la variance du vote par commune sont expliqués par le modèle, faisant substantiellement reculer la part résiduelle par rapport aux travaux précédents. Les variables politiques ont aussi les signes attendus et sont aussi significatives. Nos résultats soulignent l'importance de l'interaction entre choix électoraux et décisions de participation et l'importance de les traiter simultanément. Enfin, ils mettent en lumière les niveaux élevés d'information et de rationalité de l'électorat.

Wavelet analysis residual kriging vs. neural network residual kriging

This paper deals with the problem of spatial data mapping. A new method based on wavelet interpolation and geostatistical prediction (kriging) is proposed. The method - wavelet analysis residual kriging (WARK) - is developed in order to assess the problems rising for highly variable data in presence of spatial trends. In these cases stationary prediction models have very limited application. Wavelet analysis is used to model large-scale structures and kriging of the remaining residuals focuses on small-scale peculiarities. WARK is able to model spatial pattern which features multiscale structure. In the present work WARK is applied to the rainfall data and the results of validation are compared with the ones obtained from neural network residual kriging (NNRK). NNRK is also a residual-based method, which uses artificial neural network to model large-scale non-linear trends. The comparison of the results demonstrates the high quality performance of WARK in predicting hot spots, reproducing global statistical characteristics of the distribution and spatial correlation structure.

Improving generalized regression analysis for the spatial prediction of forest communities

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.

Continuous arterial spin labeling of mouse cerebral blood flow using an actively-detuned two-coil system at 9.4T.

Among numerous magnetic resonance imaging (MRI) techniques, perfusion MRI provides insight into the passage of blood through the brain's vascular network non-invasively. Studying disease models and transgenic mice would intrinsically help understanding the underlying brain functions, cerebrovascular disease and brain disorders. This study evaluates the feasibility of performing continuous arterial spin labeling (CASL) on all cranial arteries for mapping murine cerebral blood flow at 9.4 T. We showed that with an active-detuned two-coil system, a labeling efficiency of 0.82 ± 0.03 was achieved with minimal magnetization transfer residuals in brain. The resulting cerebral blood flow of healthy mouse was 99 ± 26 mL/100g/min, in excellent agreement with other techniques. In conclusion, high magnetic fields deliver high sensitivity and allowing not only CASL but also other MR techniques, i.e. (1)H MRS and diffusion MRI etc, in studying murine brains.

Diffusion-weighted spectroscopy: a novel approach to determine macromolecule resonances in short-echo time 1H-MRS.

Quantification of short-echo time proton magnetic resonance spectroscopy results in >18 metabolite concentrations (neurochemical profile). Their quantification accuracy depends on the assessment of the contribution of macromolecule (MM) resonances, previously experimentally achieved by exploiting the several fold difference in T(1). To minimize effects of heterogeneities in metabolites T(1), the aim of the study was to assess MM signal contributions by combining inversion recovery (IR) and diffusion-weighted proton spectroscopy at high-magnetic field (14.1 T) and short echo time (= 8 msec) in the rat brain. IR combined with diffusion weighting experiments (with δ/Δ = 1.5/200 msec and b-value = 11.8 msec/μm(2)) showed that the metabolite nulled spectrum (inversion time = 740 msec) was affected by residuals attributed to creatine, inositol, taurine, choline, N-acetylaspartate as well as glutamine and glutamate. While the metabolite residuals were significantly attenuated by 50%, the MM signals were almost not affected (< 8%). The combination of metabolite-nulled IR spectra with diffusion weighting allows a specific characterization of MM resonances with minimal metabolite signal contributions and is expected to lead to a more precise quantification of the neurochemical profile.

The application of trend surface models to the analysis of time factors in Swiss cancer mortality.

To study different temporal components on cancer mortality (age, period and cohort) methods of graphic representation were applied to Swiss mortality data from 1950 to 1984. Maps using continuous slopes ("contour maps") and based on eight tones of grey according to the absolute distribution of rates were used to represent the surfaces defined by the matrix of various age-specific rates. Further, progressively more complex regression surface equations were defined, on the basis of two independent variables (age/cohort) and a dependent one (each age-specific mortality rate). General patterns of trends in cancer mortality were thus identified, permitting definition of important cohort (e.g., upwards for lung and other tobacco-related neoplasms, or downwards for stomach) or period (e.g., downwards for intestines or thyroid cancers) effects, besides the major underlying age component. For most cancer sites, even the lower order (1st to 3rd) models utilised provided excellent fitting, allowing immediate identification of the residuals (e.g., high or low mortality points) as well as estimates of first-order interactions between the three factors, although the parameters of the main effects remained still undetermined. Thus, the method should be essentially used as summary guide to illustrate and understand the general patterns of age, period and cohort effects in (cancer) mortality, although they cannot conceptually solve the inherent problem of identifiability of the three components.

On the use of spatially discrete data to compute energy and mass balance

In many practical applications the state of field soils is monitored by recording the evolution of temperature and soil moisture at discrete depths. We theoretically investigate the systematic errors that arise when mass and energy balances are computed directly from these measurements. We show that, even with no measurement or model errors, large residuals might result when finite difference approximations are used to compute fluxes and storage term. To calculate the limits set by the use of spatially discrete measurements on the accuracy of balance closure, we derive an analytical solution to estimate the residual on the basis of the two key parameters: the penetration depth and the distance between the measurements. When the thickness of the control layer for which the balance is computed is comparable to the penetration depth of the forcing (which depends on the thermal diffusivity and on the forcing period) large residuals arise. The residual is also very sensitive to the distance between the measurements, which requires accurately controlling the position of the sensors in field experiments. We also demonstrate that, for the same experimental setup, mass residuals are sensitively larger than the energy residuals due to the nonlinearity of the moisture transport equation. Our analysis suggests that a careful assessment of the systematic mass error introduced by the use of spatially discrete data is required before using fluxes and residuals computed directly from field measurements.

Familiality and SNP heritability of age at onset and episodicity in major depressive disorder.

BACKGROUND: Strategies to dissect phenotypic and genetic heterogeneity of major depressive disorder (MDD) have mainly relied on subphenotypes, such as age at onset (AAO) and recurrence/episodicity. Yet, evidence on whether these subphenotypes are familial or heritable is scarce. The aims of this study are to investigate the familiality of AAO and episode frequency in MDD and to assess the proportion of their variance explained by common single nucleotide polymorphisms (SNP heritability). METHOD: For investigating familiality, we used 691 families with 2-5 full siblings with recurrent MDD from the DeNt study. We fitted (square root) AAO and episode count in a linear and a negative binomial mixed model, respectively, with family as random effect and adjusting for sex, age and center. The strength of familiality was assessed with intraclass correlation coefficients (ICC). For estimating SNP heritabilities, we used 3468 unrelated MDD cases from the RADIANT and GSK Munich studies. After similarly adjusting for covariates, derived residuals were used with the GREML method in GCTA (genome-wide complex trait analysis) software. RESULTS: Significant familial clustering was found for both AAO (ICC = 0.28) and episodicity (ICC = 0.07). We calculated from respective ICC estimates the maximal additive heritability of AAO (0.56) and episodicity (0.15). SNP heritability of AAO was 0.17 (p = 0.04); analysis was underpowered for calculating SNP heritability of episodicity. CONCLUSIONS: AAO and episodicity aggregate in families to a moderate and small degree, respectively. AAO is under stronger additive genetic control than episodicity. Larger samples are needed to calculate the SNP heritability of episodicity. The described statistical framework could be useful in future analyses.

Robust accelerated failure time regression

Robust estimators for accelerated failure time models with asymmetric (or symmetric) error distribution and censored observations are proposed. It is assumed that the error model belongs to a log-location-scale family of distributions and that the mean response is the parameter of interest. Since scale is a main component of mean, scale is not treated as a nuisance parameter. A three steps procedure is proposed. In the first step, an initial high breakdown point S estimate is computed. In the second step, observations that are unlikely under the estimated model are rejected or down weighted. Finally, a weighted maximum likelihood estimate is computed. To define the estimates, functions of censored residuals are replaced by their estimated conditional expectation given that the response is larger than the observed censored value. The rejection rule in the second step is based on an adaptive cut-off that, asymptotically, does not reject any observation when the data are generat ed according to the model. Therefore, the final estimate attains full efficiency at the model, with respect to the maximum likelihood estimate, while maintaining the breakdown point of the initial estimator. Asymptotic results are provided. The new procedure is evaluated with the help of Monte Carlo simulations. Two examples with real data are discussed.

Population pharmacokinetics in chronic myeloid leukaemia patients: observations under field-conditions

Introduction: Therapeutic drug monitoring (TDM) of imatinib has been increasingly proposed for chronic myeloid leukaemia (CML) patients, as several studies have found a correlation between trough concentrations (Cmin) >=1000ng/ml and improved response. The pharmacological monitoring project of EUTOS (European Treatment and Outcome Study) was launched to increase the availability of imatinib TDM, standardize labs, and validate proposed Cmin thresholds. Using the collected data, the objective of this analysis was to characterize imatinib Population pharmacokinetics (Pop-PK) in a large cohort of European patients, to quantify its variability and the influence of demographic factors and comedications, and to derive individual exposure variables suitable for further concentration-effect analyses.¦Methods: 4095 PK samples from 2478 adult patients were analyzed between 2006 and 2010 by LC-MS-MS and considered for Pop-PK analysis by NONMEM®. Model building used data from 973 patients with >=2 samples available (2590 samples). A sensitivity analysis was performed using all data. Available comedications (27%) were classified into inducers or inhibitors of P-glycoprotein, CYP3A4/5 and organic-cation-transporter-1 (hOCT-1).¦Results: A one-compartment model with linear elimination, zero-order absorption fitted the data best. Estimated Pop-PK parameters (interindividual variability, IIV %CV) for a 40-year old male patient were: clearance CL = 17.3 L/h (37.7%), volume V = 429L (51.1%), duration of absorption D1 = 3.2h. Outliers, reflecting potential compliance and time recording errors, were taken into account by estimating an IIV on the residual error (35.4%). Intra-individual residuals were 29.1% (proportional) plus ± 84.6 ng/mL (additive). Female patients had a 15.2% lower CL (14.6 L/h). A piece-wise linear effect of age estimated a CL of 18.7 L/h at 20 years, 17.3 L/h at 40 and 13.8 L/h at 60 years. These covariates explained 2% (CL) and 4.5% (V) of IIV variability. No effect of comedication was found. The sensitivity analysis expectedly estimated increased IIV, but similar fixed effect parameters.¦Conclusion: Imatinib PK was well described in a large cohort of CML patients under field conditions and results were concordant with previous studies. Patient characteristics explain only little IIV, confirming limited utility of prior dosage adjustment. As intra-variability is smaller than inter-patient variability, dose adjustment guided by TDM could however be beneficial in order to bring Cmin into a given therapeutic target.

Mood and nonmood components of perceived stress and exacerbation of Crohn's disease.

BACKGROUND: Diverse psychological factors are involved in the pathophysiology of stress. In order to devise effective intervention strategies, it is important to elucidate which factors play the most important role in the association between psychological stress and exacerbation of Crohn's disease (CD). We hypothesized that the association between perceived stress and exacerbation of CD would remain after removal of mood and anxiety components, which are largely involved in stress perception. METHODS: In all, 468 adults with CD were recruited and followed in different hospitals and private practices of Switzerland for 18 months. At inclusion, patients completed the Perceived Stress Questionnaire and anxiety and depression were assessed using the Hospital Anxiety and Depression Scale. During the follow-up, gastroenterologists assessed whether patients presented with a CD exacerbation. By means of binary logistic regression analysis, we estimated the factor by which one standard deviation of perceived stress would increase the odds of exacerbation of CD with and without controlling for anxiety and depression. RESULTS: The odds of exacerbation of CD increased by 1.85 times (95% confidence interval 1.43-2.40, P < 0.001) for 1 standard deviation of perceived stress. After removing the anxiety and depression components, the residuals of perceived stress were no longer associated with exacerbation of CD. CONCLUSIONS: The association between perceived stress and exacerbation of CD was fully attributable to the mood components, specifically anxiety and depression. Future interventional studies should evaluate the treatment of anxiety and depression as a strategy for potential prevention of CD exacerbations.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Automatic Decision-Oriented Mapping of Pollution Data

The paper deals with the development and application of the methodology for automatic mapping of pollution/contamination data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve this problem. The automatic tuning of isotropic and an anisotropic GRNN model using cross-validation procedure is presented. Results are compared with k-nearest-neighbours interpolation algorithm using independent validation data set. Quality of mapping is controlled by the analysis of raw data and the residuals using variography. Maps of probabilities of exceeding a given decision level and ?thick? isoline visualization of the uncertainties are presented as examples of decision-oriented mapping. Real case study is based on mapping of radioactively contaminated territories.