Oriented Percolation in One-dimensional 1/vertical bar x-y vertical bar(2) Percolation Models

We consider independent edge percolation models on Z, with edge occupation probabilities. We prove that oriented percolation occurs when beta > 1 provided p is chosen sufficiently close to 1, answering a question posed in Newman and Schulman (Commun. Math. Phys. 104: 547, 1986). The proof is based on multi-scale analysis.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Small-Sample Corrections for Score Tests in Birnbaum-Saunders Regressions

In this article, we deal with the issue of performing accurate small-sample inference in the Birnbaum-Saunders regression model, which can be useful for modeling lifetime or reliability data. We derive a Bartlett-type correction for the score test and numerically compare the corrected test with the usual score test and some other competitors.

Improved likelihood inference in Birnbaum-Saunders regressions

The Birnbaum-Saunders regression model is commonly used in reliability studies. We address the issue of performing inference in this class of models when the number of observations is small. Our simulation results suggest that the likelihood ratio test tends to be liberal when the sample size is small. We obtain a correction factor which reduces the size distortion of the test. Also, we consider a parametric bootstrap scheme to obtain improved critical values and improved p-values for the likelihood ratio test. The numerical results show that the modified tests are more reliable in finite samples than the usual likelihood ratio test. We also present an empirical application. (C) 2009 Elsevier B.V. All rights reserved.

Distribution of ABCB1 polymorphisms among Brazilians: impact of population admixture

Introduction: Interethnic admixture is a source of cryptic population structure that may lead to spurious genotype-phenotype associations in pharmacogenomic studies. We studied the impact of population stratification on the distribution of ABCB1 polymorphisms (1236C > T, 2677G > T/A and 3435C > T) among Brazilians, a highly admixed population with Amerindian, European and African ancestral roots. Methods: Individual DNA from 320 healthy adults was genotyped with a panel of ancestry informative markers, and the proportions of African component of ancestry (ACA) were estimated. ABCB1 genotypes were determined by the single base extension/termination method. We describe the association between ABCB1 polymorphisms and ACA by fitting a linear proportional odds logistic regression model to the data. Results: The distribution of the ABCB1 2677G > T/A and 3435C > T, but not the 1236C > T, SNPs displayed a significant trend for decreasing frequency of the T alleles and TT genotypes from White to Intermediate to Black individuals. The same trend was observed in the frequency of the T/nonG/T haplotype at the 1236, 2677 and 3435 loci. When the population sample was proportioned in quartiles, according to the individual ACA estimates, the frequency of the T allele and TT genotype at each locus declined progressively from the lowest (< 0.25 ACA) to the highest (> 0.75 ACA) quartile. Linear proportional odds logistic regression analysis confirmed that the odds of having the T allele at each locus decreases in a continuous manner with the increase of the ACA, throughout the ACA range (0.13-0.94) observed in the overall population sample. A significant association was also detected between the individual ACA estimates and the presence of the T/nonG/T haplotype in the overall population. Conclusion: Self-identification according to the racial/color categories proposed by the Brazilian Census is insufficient to properly control for population stratification in pharmacogenomic studies of ABCB1.

Pharmacogenetics of Warfarin: Development of a Dosing Algorithm for Brazilian Patients

A dosing algorithm including genetic (VKORC1 and CYP2C9 genotypes) and nongenetic factors (age, weight, therapeutic indication, and cotreatment with amiodarone or simvastatin) explained 51% of the variance in stable weekly warfarin doses in 390 patients attending an anticoagulant clinic in a Brazilian public hospital. The VKORC1 3673G>A genotype was the most important predictor of warfarin dose, with a partial R(2) value of 23.9%. Replacing the VKORC1 3673G>A genotype with VKORC1 diplotype did not increase the algorithm`s predictive power. We suggest that three other single-nucleotide polymorphisms (SNPs) (5808T>G, 6853G>C, and 9041G>A) that are in strong linkage disequilibrium (LD) with 3673G>A would be equally good predictors of the warfarin dose requirement. The algorithm`s predictive power was similar across the self-identified ""race/color"" subsets. ""Race/color"" was not associated with stable warfarin dose in the multiple regression model, although the required warfarin dose was significantly lower (P = 0.006) in white (29 +/- 13 mg/week, n = 196) than in black patients (35 +/- 15 mg/week, n = 76).

Is there wage premium to computer use in Sweden

This paper examines the wage premium to computer use in Sweden in the early 1990’s. I use simple regression model and interaction terms in my paper to examine the effect of computer use at work. Although the data is only one-year cross-section data, my results clearly show a wage premium to computer use in Sweden. There are also interesting findings in my paper by using Swedish data. From the results, I find wage premium to be related to intensity of computer use at work.

Design/Evaluation of A Methodology For Performance Optimization Of Indexable Carbide Inserts

In this project, two broad facets in the design of a methodology for performance optimization of indexable carbide inserts were examined. They were physical destructive testing and software simulation.For the physical testing, statistical research techniques were used for the design of the methodology. A five step method which began with Problem definition, through System identification, Statistical model formation, Data collection and Statistical analyses and results was indepthly elaborated upon. Set-up and execution of an experiment with a compression machine together with roadblocks and possible solution to curb road blocks to quality data collection were examined. 2k factorial design was illustrated and recommended for process improvement. Instances of first-order and second-order response surface analyses were encountered. In the case of curvature, test for curvature significance with center point analysis was recommended. Process optimization with method of steepest ascent and central composite design or process robustness studies of response surface analyses were also recommended.For the simulation test, AdvantEdge program was identified as the most used software for tool development. Challenges to the efficient application of this software were identified and possible solutions proposed. In conclusion, software simulation and physical testing were recommended to meet the objective of the project.

Defining Relevant Product Markets for Pharmaceuticals

To identify the relevant product markets for Swedish pharmaceuticals, a spatial econometrics approach is employed. First, we calculate Moran’s Is for different market definitions and then we use a spatial Durbin model to determine the effect of price changes on quantity sold off own and competing products. As expected, the results show that competition is strongest between close substitutes; however, the relevant product markets for Swedish pharmaceuticals extend beyond close substitutes down to products included in the same class on the four-digit level of the Anatomic Therapeutic Chemical system as defined by the World Health Organization. The spatial regression model further indicates that increases in the price of a product significantly lower the quantity sold of that product and in the same time increase the quantity sold of competing products. For close substitutes (products belonging to the same class on the seven-digit level of the Anatomic Therapeutic Chemical system), as well as for products that, without being close substitutes, belong to the same therapeutic/pharmacological/chemical subgroup (the same class on the five-digit level of the Anatomic Therapeutic Chemical system), a significant change towards increased competition is also visible after 1 July 2009 when the latest policy changes with regards to pharmaceuticals have been implemented in Sweden.

Computerized identification of motor complications in Parkinson's disease

Objective: To investigate whether spirography-based objective measures are able to effectively characterize the severity of unwanted symptom states (Off and dyskinesia) and discriminate them from motor state of healthy elderly subjects. Background: Sixty-five patients with advanced Parkinson’s disease (PD) and 10 healthy elderly (HE) subjects performed repeated assessments of spirography, using a touch screen telemetry device in their home environments. On inclusion, the patients were either treated with levodopa-carbidopa intestinal gel or were candidates for switching to this treatment. On each test occasion, the subjects were asked trace a pre-drawn Archimedes spiral shown on the screen, using an ergonomic pen stylus. The test was repeated three times and was performed using dominant hand. A clinician used a web interface which animated the spiral drawings, allowing him to observe different kinematic features, like accelerations and spatial changes, during the drawing process and to rate different motor impairments. Initially, the motor impairments of drawing speed, irregularity and hesitation were rated on a 0 (normal) to 4 (extremely severe) scales followed by marking the momentary motor state of the patient into 2 categories that is Off and Dyskinesia. A sample of spirals drawn by HE subjects was randomly selected and used in subsequent analysis. Methods: The raw spiral data, consisting of stylus position and timestamp, were processed using time series analysis techniques like discrete wavelet transform, approximate entropy and dynamic time warping in order to extract 13 quantitative measures for representing meaningful motor impairment information. A principal component analysis (PCA) was used to reduce the dimensions of the quantitative measures into 4 principal components (PC). In order to classify the motor states into 3 categories that is Off, HE and dyskinesia, a logistic regression model was used as a classifier to map the 4 PCs to the corresponding clinically assigned motor state categories. A stratified 10-fold cross-validation (also known as rotation estimation) was applied to assess the generalization ability of the logistic regression classifier to future independent data sets. To investigate mean differences of the 4 PCs across the three categories, a one-way ANOVA test followed by Tukey multiple comparisons was used. Results: The agreements between computed and clinician ratings were very good with a weighted area under the receiver operating characteristic curve (AUC) coefficient of 0.91. The mean PC scores were different across the three motor state categories, only at different levels. The first 2 PCs were good at discriminating between the motor states whereas the PC3 was good at discriminating between HE subjects and PD patients. The mean scores of PC4 showed a trend across the three states but without significant differences. The Spearman’s rank correlations between the first 2 PCs and clinically assessed motor impairments were as follows: drawing speed (PC1, 0.34; PC2, 0.83), irregularity (PC1, 0.17; PC2, 0.17), and hesitation (PC1, 0.27; PC2, 0.77). Conclusions: These findings suggest that spirography-based objective measures are valid measures of spatial- and time-dependent deficits and can be used to distinguish drug-related motor dysfunctions between Off and dyskinesia in PD. These measures can be potentially useful during clinical evaluation of individualized drug-related complications such as over- and under-medications thus maximizing the amount of time the patients spend in the On state.

Determinants and effects of central bank independence reforms

This thesis consists of four empirically oriented papers on central bank independence (CBI) reforms.    Paper [1] is an investigation of why politicians around the world have chosen to give up power to independent central banks, thereby reducing their ability to control the economy. A new data-set, including the possible occurrence of CBI-reforms in 132 countries during 1980-2005, was collected. Politicians in non-OECD countries were more likely to delegate power to independent central banks if their country had been characterized by high variability in inflation and if they faced a high probability of being replaced. No such effects were found for OECD countries.    Paper [2], using a difference-in-difference approach, studies whether CBI reform matters for inflation performance. The analysis is based on a dataset including the possible occurrence of CBI-reforms in 132 countries during the period of 1980-2005. CBI reform is found to have contributed to bringing down inflation in high-inflation countries, but it seems unrelated to inflation performance in low-inflation countries.    Paper [3] investigates whether CBI-reforms are important in reducing inflation and maintaining price stability, using a random-effects random-coefficients model to account for heterogeneity in the effects of CBI-reforms on inflation. CBI-reforms are found to have reduced inflation on average by 3.31 percent, but the effect is only present when countries with historically high inflation rates are included in the sample. Countries with more modest inflation rates have achieved low inflation without institutional reforms that grant central banks more independence, thus undermining the time-inconsistency theory case for CBI. There is furthermore no evidence that CBI-reforms have contributed to lower inflation variability    Paper [4] studies the relationship between CBI and a suggested trade-off between price variability and output variability using data on CBI-levels, and data the on implementation dates of CBI-reforms. The results question the existence of such a trade-off, but indicate that there may still be potential gains in stabilization policy from CBI-reforms.

Modelling dominance in a flexible intercross analysis

The aim of this paper is to develop a flexible model for analysis of quantitative trait loci (QTL) in outbred line crosses, which includes both additive and dominance effects. Our flexible intercross analysis (FIA) model accounts for QTL that are not fixed within founder lines and is based on the variance component framework. Genome scans with FIA are performed using a score statistic, which does not require variance component estimation. RESULTS: Simulations of a pedigree with 800 F2 individuals showed that the power of FIA including both additive and dominance effects was almost 50% for a QTL with equal allele frequencies in both lines with complete dominance and a moderate effect, whereas the power of a traditional regression model was equal to the chosen significance value of 5%. The power of FIA without dominance effects included in the model was close to those obtained for FIA with dominance for all simulated cases except for QTL with overdominant effects. A genome-wide linkage analysis of experimental data from an F2 intercross between Red Jungle Fowl and White Leghorn was performed with both additive and dominance effects included in FIA. The score values for chicken body weight at 200 days of age were similar to those obtained in FIA analysis without dominance. CONCLUSION: We have extended FIA to include QTL dominance effects. The power of FIA was superior, or similar, to standard regression methods for QTL effects with dominance. The difference in power for FIA with or without dominance is expected to be small as long as the QTL effects are not overdominant. We suggest that FIA with only additive effects should be the standard model to be used, especially since it is more computationally efficient.

Fitting spatial models in the R package: hglm

We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.

Nurses' research utilization two years after graduation : a national survey ofassociated individual, organizational, and educational factors

Background. Nurses' research utilization (RU) as part of evidence-based practice is strongly emphasized in today's nursing education and clinical practice. The primary aim of RU is to provide high-quality nursing care to patients. Data on newly graduated nurses' RU are scarce, but a predominance of low use has been reported in recent studies. Factors associated with nurses' RU have previously been identified among individual and organizational/contextual factors, but there is a lack of knowledge about how these factors, including educational ones, interact with each other and with RU, particularly in nurses during the first years after graduation. The purpose of this study was therefore to identify factors that predict the probability for low RU among registered nurses two years after graduation. Methods. Data were collected as part of the LANE study (Longitudinal Analysis of Nursing Education), a Swedish national survey of nursing students and registered nurses. Data on nurses' instrumental, conceptual, and persuasive RU were collected two years after graduation (2007, n = 845), together with data on work contextual factors. Data on individual and educational factors were collected in the first year (2002) and last term of education (2004). Guided by an analytic schedule, bivariate analyses, followed by logistic regression modeling, were applied. Results. Of the variables associated with RU in the bivariate analyses, six were found to be significantly related to low RU in the final logistic regression model: work in the psychiatric setting, role ambiguity, sufficient staffing, low work challenge, being male, and low student activity. Conclusions. A number of factors associated with nurses' low extent of RU two years postgraduation were found, most of them potentially modifiable. These findings illustrate the multitude of factors related to low RU extent and take their interrelationships into account. This knowledge might serve as useful input in planning future studies aiming to improve nurses', specifically newly graduated nurses', RU.