943 resultados para non-parametric statistics


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OBJECTIVES: To analyse the prevalence of lifetime recourse to prostitution (LRP) among men in the general population of Switzerland from a trend and cohort perspective. METHODS: Using nine repeated representative cross-sectional surveys from 1987 to 2000, age-specific estimates of LRP were computed. Trends and period effect were analysed as the evolution of cross-sectional population estimates within age groups and overall. Cohort analysis relied on cohorts constructed from the 1989 survey and followed in subsequent waves. Age and cohort effects were modelled using logistic regression and non-parametric monotone regression. RESULTS: Whereas prevalence for the younger groups was found to be logically lower, there was no consistent increasing or decreasing trend over the years; there was no significant period effect. For the 17-30 year age group, the mean estimate over 1987-2000 was 11.5% (range 8.3 to 12.7%); for the 31-45 year group, the mean was 21.5% (range over 1989-2000 20.3 to 23.0%). Regarding cohort analysis, the prevalence of LRP was found to increase steeply in the youngest ages before reaching a plateau near the age of 40 years. At the age of 43 years, the prevalence was estimated to be 22.6% (95% CI 21.1% to 24.1%). CONCLUSIONS: The steep increase in the cohort-wise prevalence of LRP in younger ages calls for a concentration of prevention activities in young people. If the plateauing at approximately 40 years of age is not followed by a further increase later in life, which is not known, then consumers of paid sex would be repeat buyers only, a fact that should be taken into account by prevention.

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Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

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In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.

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INTRODUCTION AND AIMS: Satisfaction of inpatients with served food within a hospital care system still constitutes one of the main attempts to modernize food services. The impact of type of menu, food category, hospital centre and timetable on the meals wastage produced in different Spanish healthcare settings, was evaluated. METHODS: Meal wastage was measured through a semiquantitative 5-point scale ("nothing on plate"; "¼ on plate"; "half on plate"; "¾ on plate" and "all on plate"). The study was carried out in two periods of three months each in 2010 and 2011. A trained person took charge of measuring plate waste classified into 726 servings belonging to 11 menus. In total 31,392 plates were served to 7,868 inpatients. A Kruskal-Wallis non-parametric test (p < 0.05) was applied to evaluate significant differences among the variables studied. RESULTS: The menus were satisfactorily consumed because more than 50% of the plates were classified as "nothing on plate". Regarding food categories, 26.78% of the plates corresponded to soups and purées, while pasta and rice, and prepared foods were only distributed in 4-5% of the servings. Desserts were mostly consumed, while cooked vegetables were less accepted by the inpatients evaluated. Other factors such as hospital centre influenced plate waste (p < 0.05) but timetable did not (p > 0.05). CONCLUSION: Visual inspections of plate waste might be useful to optimize type and quality of menus served. The type of menu served and the food category could have a great influence on food acceptability by the inpatientsstudied.

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This paper presents a comparative analysis of linear and mixed modelsfor short term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay.The series is interpolated with a linear predictor which minimizes theforecast mean square error. The linear models are seasonal ARIMA models and themixed models have a linear component and a non linear seasonal component.The non linear component is estimated by a non parametric regression of dataversus time. Short term forecasts, no more than two days ahead, are of interestbecause they can be used by the port authorities to notice the fleet.Several models are fitted and compared by their forecasting behavior.

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Given $n$ independent replicates of a jointly distributed pair $(X,Y)\in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessingthe {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk.The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover.Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent,and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension.For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.

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La regressió basada en distàncies és un mètode de predicció que consisteix en dos passos: a partir de les distàncies entre observacions obtenim les variables latents, les quals passen a ser els regressors en un model lineal de mínims quadrats ordinaris. Les distàncies les calculem a partir dels predictors originals fent us d'una funció de dissimilaritats adequada. Donat que, en general, els regressors estan relacionats de manera no lineal amb la resposta, la seva selecció amb el test F usual no és possible. En aquest treball proposem una solució a aquest problema de selecció de predictors definint tests estadístics generalitzats i adaptant un mètode de bootstrap no paramètric per a l'estimació dels p-valors. Incluim un exemple numèric amb dades de l'assegurança d'automòbils.

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La regressió basada en distàncies és un mètode de predicció que consisteix en dos passos: a partir de les distàncies entre observacions obtenim les variables latents, les quals passen a ser els regressors en un model lineal de mínims quadrats ordinaris. Les distàncies les calculem a partir dels predictors originals fent us d'una funció de dissimilaritats adequada. Donat que, en general, els regressors estan relacionats de manera no lineal amb la resposta, la seva selecció amb el test F usual no és possible. En aquest treball proposem una solució a aquest problema de selecció de predictors definint tests estadístics generalitzats i adaptant un mètode de bootstrap no paramètric per a l'estimació dels p-valors. Incluim un exemple numèric amb dades de l'assegurança d'automòbils.

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A parametric procedure for the blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Experiments show that the proposed algorithms perform efficiently, even in the presence of hard distortion. The method, based on the minimization of the output mutual information, needs the knowledge of log-derivative of input distribution (the so-called score function). Each algorithm consists of three adaptive blocks: one devoted to adaptive estimation of the score function, and two other blocks estimating the inverses of the linear and nonlinear parts of the channel, (quasi-)optimally adapted using the estimated score functions. This paper is mainly concerned by the nonlinear part, for which we propose two parametric models, the first based on a polynomial model and the second on a neural network, while [14, 15] proposed non-parametric approaches.

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Background: HAART has contributed to decrease the HIV-related mortality and morbidity. However, the prevalence of HIV-associated neurocognitive disorders (HAND) seems to have increased. The aim of this study was to determine the prevalence of cognitive complaint and of HAND in a cohort of aviremic HIV_patients in the South-western part of Switzerland. Design/Methods: Two hundred HIV_ patients who had (1) undetectable HIV RNA concentrations in the plasma for_3 months, (2) no history of major opportunistic infection of the CNS in the past three years, (3) no current use of IV drugs and (4) no signs of major depression according to the DSM-IV criteria, answered a questionnaire designed to elicit cognitive complaints. Cognitive functions of a subset of HIV_ patients with or without cognitive complaints were assessed using the HIV Dementia scale (HDS) and a battery of neuropsychological tests evaluating the sub-cortical functions. Cognitive impairment was defined according to the revised diagnostic criteria for HAND. Non-parametric tests were used for statistics and a Bonferroni corrected standard p level of pB0.002 was applied for multiple comparisons. Results: The prevalence of cognitive complaints was 27% (54 patients) among the 200 questioned patients. At the time of writing this abstract, cognitive functions of 50 complaining and 28 noncomplaining aviremic patients had been assessed with the HDS and the full neuropsychological battery. The prevalence of HAND producing at least mild interference in daily functioning (mild neurocognitive disorders [MND] or HIV-associated dementia [HAD]) was 44% (34/78 patients) in the group who underwent neuropsychological testing. Objective evidences of HAND were more frequent in complaining than in non-complaining patients (pB0.001). Using a ROC curve, a cut-off of 13 on the HDS was found to have a sensitivity of 74% and a specificity of 71% (p_0.001) for the diagnosis of HAND. A trend for lower CNS Penetrating-Effectiveness scores for HAART in patients with MND or HAD as compared to the others was present (1.59 0.6 vs. 1.990.6; p_0.006 [Bonferroni correction]). Conclusions/Relevance: So far, our results suggest that (1) the prevalence of HAND is high in HIV_ patients with a long-term suppression of viremia, and (2) cognitive complaints expressed by aviremic HIV_ patients should be carefully investigated as they correlate with objective evidences of cognitive decline in a neuropsychological testing. HAART with a high CNS penetrating-effectiveness may contribute to prevent HAND. Funding: Swiss HIV Cohort Study.

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We propose new methods for evaluating predictive densities that focus on the models' actual predictive ability in finite samples. The tests offer a simple way of evaluatingthe correct specification of predictive densities, either parametric or non-parametric.The results indicate that our tests are well sized and have good power in detecting mis-specification in predictive densities. An empirical application to the Survey ofProfessional Forecasters and a baseline Dynamic Stochastic General Equilibrium modelshows the usefulness of our methodology.

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Tämän tutkielman tavoitteena on selvittää Venäjän, Slovakian, Tsekin, Romanian, Bulgarian, Unkarin ja Puolan osakemarkkinoiden heikkojen ehtojen tehokkuutta. Tämä tutkielma on kvantitatiivinen tutkimus ja päiväkohtaiset indeksin sulkemisarvot kerättiin Datastreamin tietokannasta. Data kerättiin pörssien ensimmäisestä kaupankäyntipäivästä aina vuoden 2006 elokuun loppuun saakka. Analysoinnin tehostamiseksi dataa tutkittiin koko aineistolla, sekä kahdella aliperiodilla. Osakemarkkinoiden tehokkuutta on testattu neljällä tilastollisella metodilla, mukaan lukien autokorrelaatiotesti ja epäparametrinen runs-testi. Tavoitteena on myös selvittääesiintyykö kyseisillä markkinoilla viikonpäiväanomalia. Viikonpäiväanomalian esiintymistä tutkitaan käyttämällä pienimmän neliösumman menetelmää (OLS). Viikonpäiväanomalia on löydettävissä kaikilta edellä mainituilta osakemarkkinoilta paitsi Tsekin markkinoilta. Merkittävää, positiivista tai negatiivista autokorrelaatiota, on löydettävissä kaikilta osakemarkkinoilta, myös Ljung-Box testi osoittaa kaikkien markkinoiden tehottomuutta täydellä periodilla. Osakemarkkinoiden satunnaiskulku hylätään runs-testin perusteella kaikilta muilta paitsi Slovakian osakemarkkinoilla, ainakin tarkastellessa koko aineistoa tai ensimmäistä aliperiodia. Aineisto ei myöskään ole normaalijakautunut minkään indeksin tai aikajakson kohdalla. Nämä havainnot osoittavat, että kyseessä olevat markkinat eivät ole heikkojen ehtojen mukaan tehokkaita

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Biological scaling analyses employing the widely used bivariate allometric model are beset by at least four interacting problems: (1) choice of an appropriate best-fit line with due attention to the influence of outliers; (2) objective recognition of divergent subsets in the data (allometric grades); (3) potential restrictions on statistical independence resulting from phylogenetic inertia; and (4) the need for extreme caution in inferring causation from correlation. A new non-parametric line-fitting technique has been developed that eliminates requirements for normality of distribution, greatly reduces the influence of outliers and permits objective recognition of grade shifts in substantial datasets. This technique is applied in scaling analyses of mammalian gestation periods and of neonatal body mass in primates. These analyses feed into a re-examination, conducted with partial correlation analysis, of the maternal energy hypothesis relating to mammalian brain evolution, which suggests links between body size and brain size in neonates and adults, gestation period and basal metabolic rate. Much has been made of the potential problem of phylogenetic inertia as a confounding factor in scaling analyses. However, this problem may be less severe than suspected earlier because nested analyses of variance conducted on residual variation (rather than on raw values) reveals that there is considerable variance at low taxonomic levels. In fact, limited divergence in body size between closely related species is one of the prime examples of phylogenetic inertia. One common approach to eliminating perceived problems of phylogenetic inertia in allometric analyses has been calculation of 'independent contrast values'. It is demonstrated that the reasoning behind this approach is flawed in several ways. Calculation of contrast values for closely related species of similar body size is, in fact, highly questionable, particularly when there are major deviations from the best-fit line for the scaling relationship under scrutiny.

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Theultimate goal of any research in the mechanism/kinematic/design area may be called predictive design, ie the optimisation of mechanism proportions in the design stage without requiring extensive life and wear testing. This is an ambitious goal and can be realised through development and refinement of numerical (computational) technology in order to facilitate the design analysis and optimisation of complex mechanisms, mechanical components and systems. As a part of the systematic design methodology this thesis concentrates on kinematic synthesis (kinematic design and analysis) methods in the mechanism synthesis process. The main task of kinematic design is to find all possible solutions in the form of structural parameters to accomplish the desired requirements of motion. Main formulations of kinematic design can be broadly divided to exact synthesis and approximate synthesis formulations. The exact synthesis formulation is based in solving n linear or nonlinear equations in n variables and the solutions for the problem areget by adopting closed form classical or modern algebraic solution methods or using numerical solution methods based on the polynomial continuation or homotopy. The approximate synthesis formulations is based on minimising the approximation error by direct optimisation The main drawbacks of exact synthesis formulationare: (ia) limitations of number of design specifications and (iia) failure in handling design constraints- especially inequality constraints. The main drawbacks of approximate synthesis formulations are: (ib) it is difficult to choose a proper initial linkage and (iib) it is hard to find more than one solution. Recentformulations in solving the approximate synthesis problem adopts polynomial continuation providing several solutions, but it can not handle inequality const-raints. Based on the practical design needs the mixed exact-approximate position synthesis with two exact and an unlimited number of approximate positions has also been developed. The solutions space is presented as a ground pivot map but thepole between the exact positions cannot be selected as a ground pivot. In this thesis the exact synthesis problem of planar mechanism is solved by generating all possible solutions for the optimisation process ¿ including solutions in positive dimensional solution sets - within inequality constraints of structural parameters. Through the literature research it is first shown that the algebraic and numerical solution methods ¿ used in the research area of computational kinematics ¿ are capable of solving non-parametric algebraic systems of n equations inn variables and cannot handle the singularities associated with positive-dimensional solution sets. In this thesis the problem of positive-dimensional solutionsets is solved adopting the main principles from mathematical research area of algebraic geometry in solving parametric ( in the mathematical sense that all parameter values are considered ¿ including the degenerate cases ¿ for which the system is solvable ) algebraic systems of n equations and at least n+1 variables.Adopting the developed solution method in solving the dyadic equations in direct polynomial form in two- to three-precision-points it has been algebraically proved and numerically demonstrated that the map of the ground pivots is ambiguousand that the singularities associated with positive-dimensional solution sets can be solved. The positive-dimensional solution sets associated with the poles might contain physically meaningful solutions in the form of optimal defectfree mechanisms. Traditionally the mechanism optimisation of hydraulically driven boommechanisms is done at early state of the design process. This will result in optimal component design rather than optimal system level design. Modern mechanismoptimisation at system level demands integration of kinematic design methods with mechanical system simulation techniques. In this thesis a new kinematic design method for hydraulically driven boom mechanism is developed and integrated in mechanical system simulation techniques. The developed kinematic design method is based on the combinations of two-precision-point formulation and on optimisation ( with mathematical programming techniques or adopting optimisation methods based on probability and statistics ) of substructures using calculated criteria from the system level response of multidegree-of-freedom mechanisms. Eg. by adopting the mixed exact-approximate position synthesis in direct optimisation (using mathematical programming techniques) with two exact positions and an unlimitednumber of approximate positions the drawbacks of (ia)-(iib) has been cancelled.The design principles of the developed method are based on the design-tree -approach of the mechanical systems and the design method ¿ in principle ¿ is capable of capturing the interrelationship between kinematic and dynamic synthesis simultaneously when the developed kinematic design method is integrated with the mechanical system simulation techniques.

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Non-adaptive personality traits may constitute risk factors for development of psychiatric disorders such as depression and anxiety. We aim to evaluate associations and the predictive value of personality traits among alcohol-dependent individuals, with or without psychiatric comorbidities. The convenience sample comprised two groups of males over 18 years of age: one with subjects who had an alcohol dependence diagnosis (AG, n=110), and a control group without abuse and/or alcohol dependence diagnosis (CG, n=110). The groups were assessed by means of the Structured Clinical Interview DSM-IV (SCID-IV). AG participants were recruited among outpatients from the university hospital, whereas CG participants were recruited from a primary healthcare program. Data collection was done individually with self-assessment instruments. Parametric statistics were performed, and a significance level of P=0.05 was adopted. A positive correlation was observed between openness and the length of time that alcohol has been consumed, as were significant and negative correlations between conscientiousness and both the length of time alcohol has been consumed and the number of doses. For alcoholics, extraversion emerged as a protective factor against depression development (P=0.008) and tobacco abuse (P=0.007), whereas openness worked as a protective factor against anxiety (P=0.02). The findings point to specific deficits presented by alcoholics in relation to personality traits with or without psychiatric comorbidities and to the understanding that therapeutic approaches should favor procedures and/or preventive measures that allow more refined awareness about the disorder.