996 resultados para covariance model


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This work presents Bayes invariant quadratic unbiased estimator, for short BAIQUE. Bayesian approach is used here to estimate the covariance functions of the regionalized variables which appear in the spatial covariance structure in mixed linear model. Firstly a brief review of spatial process, variance covariance components structure and Bayesian inference is given, since this project deals with these concepts. Then the linear equations model corresponding to BAIQUE in the general case is formulated. That Bayes estimator of variance components with too many unknown parameters is complicated to be solved analytically. Hence, in order to facilitate the handling with this system, BAIQUE of spatial covariance model with two parameters is considered. Bayesian estimation arises as a solution of a linear equations system which requires the linearity of the covariance functions in the parameters. Here the availability of prior information on the parameters is assumed. This information includes apriori distribution functions which enable to find the first and the second moments matrix. The Bayesian estimation suggested here depends only on the second moment of the prior distribution. The estimation appears as a quadratic form y'Ay , where y is the vector of filtered data observations. This quadratic estimator is used to estimate the linear function of unknown variance components. The matrix A of BAIQUE plays an important role. If such a symmetrical matrix exists, then Bayes risk becomes minimal and the unbiasedness conditions are fulfilled. Therefore, the symmetry of this matrix is elaborated in this work. Through dealing with the infinite series of matrices, a representation of the matrix A is obtained which shows the symmetry of A. In this context, the largest singular value of the decomposed matrix of the infinite series is considered to deal with the convergence condition and also it is connected with Gerschgorin Discs and Poincare theorem. Then the BAIQUE model for some experimental designs is computed and compared. The comparison deals with different aspects, such as the influence of the position of the design points in a fixed interval. The designs that are considered are those with their points distributed in the interval [0, 1]. These experimental structures are compared with respect to the Bayes risk and norms of the matrices corresponding to distances, covariance structures and matrices which have to satisfy the convergence condition. Also different types of the regression functions and distance measurements are handled. The influence of scaling on the design points is studied, moreover, the influence of the covariance structure on the best design is investigated and different covariance structures are considered. Finally, BAIQUE is applied for real data. The corresponding outcomes are compared with the results of other methods for the same data. Thereby, the special BAIQUE, which estimates the general variance of the data, achieves a very close result to the classical empirical variance.

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The role of clinical chemistry has traditionally been to evaluate acutely ill or hospitalized patients. Traditional statistical methods have serious drawbacks in that they use univariate techniques. To demonstrate alternative methodology, a multivariate analysis of covariance model was developed and applied to the data from the Cooperative Study of Sickle Cell Disease.^ The purpose of developing the model for the laboratory data from the CSSCD was to evaluate the comparability of the results from the different clinics. Several variables were incorporated into the model in order to control for possible differences among the clinics that might confound any real laboratory differences.^ Differences for LDH, alkaline phosphatase and SGOT were identified which will necessitate adjustments by clinic whenever these data are used. In addition, aberrant clinic values for LDH, creatinine and BUN were also identified.^ The use of any statistical technique including multivariate analysis without thoughtful consideration may lead to spurious conclusions that may not be corrected for some time, if ever. However, the advantages of multivariate analysis far outweigh its potential problems. If its use increases as it should, the applicability to the analysis of laboratory data in prospective patient monitoring, quality control programs, and interpretation of data from cooperative studies could well have a major impact on the health and well being of a large number of individuals. ^

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In this paper we discuss a fast Bayesian extension to kriging algorithms which has been used successfully for fast, automatic mapping in emergency conditions in the Spatial Interpolation Comparison 2004 (SIC2004) exercise. The application of kriging to automatic mapping raises several issues such as robustness, scalability, speed and parameter estimation. Various ad-hoc solutions have been proposed and used extensively but they lack a sound theoretical basis. In this paper we show how observations can be projected onto a representative subset of the data, without losing significant information. This allows the complexity of the algorithm to grow as O(n m 2), where n is the total number of observations and m is the size of the subset of the observations retained for prediction. The main contribution of this paper is to further extend this projective method through the application of space-limited covariance functions, which can be used as an alternative to the commonly used covariance models. In many real world applications the correlation between observations essentially vanishes beyond a certain separation distance. Thus it makes sense to use a covariance model that encompasses this belief since this leads to sparse covariance matrices for which optimised sparse matrix techniques can be used. In the presence of extreme values we show that space-limited covariance functions offer an additional benefit, they maintain the smoothness locally but at the same time lead to a more robust, and compact, global model. We show the performance of this technique coupled with the sparse extension to the kriging algorithm on synthetic data and outline a number of computational benefits such an approach brings. To test the relevance to automatic mapping we apply the method to the data used in a recent comparison of interpolation techniques (SIC2004) to map the levels of background ambient gamma radiation. © Springer-Verlag 2007.

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Simulated-annealing-based conditional simulations provide a flexible means of quantitatively integrating diverse types of subsurface data. Although such techniques are being increasingly used in hydrocarbon reservoir characterization studies, their potential in environmental, engineering and hydrological investigations is still largely unexploited. Here, we introduce a novel simulated annealing (SA) algorithm geared towards the integration of high-resolution geophysical and hydrological data which, compared to more conventional approaches, provides significant advancements in the way that large-scale structural information in the geophysical data is accounted for. Model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter conditioned to the geophysical data. This is the only place where geophysical information is utilized in our algorithm, which is in marked contrast to other approaches where model perturbations are made through the swapping of values in the simulation grid and agreement with soft data is enforced through a correlation coefficient constraint. Another major feature of our algorithm is the way in which available geostatistical information is utilized. Instead of constraining realizations to match a parametric target covariance model over a wide range of spatial lags, we constrain the realizations only at smaller lags where the available geophysical data cannot provide enough information. Thus we allow the larger-scale subsurface features resolved by the geophysical data to have much more due control on the output realizations. Further, since the only component of the SA objective function required in our approach is a covariance constraint at small lags, our method has improved convergence and computational efficiency over more traditional methods. Here, we present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on a synthetic data set, and then applied to data collected at the Boise Hydrogeophysical Research Site.

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The issue of smoothing in kriging has been addressed either by estimation or simulation. The solution via estimation calls for postprocessing kriging estimates in order to correct the smoothing effect. Stochastic simulation provides equiprobable images presenting no smoothing and reproducing the covariance model. Consequently, these images reproduce both the sample histogram and the sample semivariogram. However, there is still a problem, which is the lack of local accuracy of simulated images. In this paper, a postprocessing algorithm for correcting the smoothing effect of ordinary kriging estimates is compared with sequential Gaussian simulation realizations. Based on samples drawn from exhaustive data sets, the postprocessing algorithm is shown to be superior to any individual simulation realization yet, at the expense of providing one deterministic estimate of the random function.

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This thesis contributes to the heuristic optimization of the p-median problem and Swedish population redistribution.   The p-median model is the most representative model in the location analysis. When facilities are located to a population geographically distributed in Q demand points, the p-median model systematically considers all the demand points such that each demand point will have an effect on the decision of the location. However, a series of questions arise. How do we measure the distances? Does the number of facilities to be located have a strong impact on the result? What scale of the network is suitable? How good is our solution? We have scrutinized a lot of issues like those. The reason why we are interested in those questions is that there are a lot of uncertainties in the solutions. We cannot guarantee our solution is good enough for making decisions. The technique of heuristic optimization is formulated in the thesis.   Swedish population redistribution is examined by a spatio-temporal covariance model. A descriptive analysis is not always enough to describe the moving effects from the neighbouring population. A correlation or a covariance analysis is more explicit to show the tendencies. Similarly, the optimization technique of the parameter estimation is required and is executed in the frame of statistical modeling. 

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The presence or absence of motorized boats, partnerships and multispecies catches characterize the fisheries of Sao Francisco River, Minas Gerais, Brazil. Fishing activity based on 109 interviews, carried out in the wet (high water: February and March) and dry (low water: July and August) seasons, with professional fishermen are described. Aiming to identify the fishery income components, a covariance model was proposed, with the income as the response variable, related to the factors: fishing ground, use of motorized or paddle boat; seasonality; presence of fishing assistant, and the following covariates: capture in weight in the week Frier to the interview, fisherman experience in yrs; and distance (km) travelled for fishing. The results indicated that the main contributions to income were the absence of an engine (because of high price of the fuel), the absence of a partner (because of low capture) and the amount of fish caught by the fishermen.

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We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

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Objective. To determine the influence of socioeconomic factors on disease activity in a Latin American (LA) early rheumatoid arthritis (RA) multinational inception cohort at baseline. Methods. Clinical evaluation, ethnicity, socioeconomic status (SES), 4-variable Disease Activity Score in 28 joints using the erythrocyte sedimentation rate (DAS28-ESR), Health Assessment Questionnaire (HAQ) disability index (DI), and erosions were recorded in 1,093 patients with early RA (<1 year from onset). Multivariate analyses evaluated influences of sex, age, marital status, education, medical coverage, SES, and ethnicity on HAQ DI, DAS28-ESR, and presence of erosions. Results. Ethnicities included 43% Mestizo, 31% Caucasian, 19% African LA, 4% Amerindian, and 3% other. Fifty-eight percent were of low/low-middle SES, 42% had <8 years of education, 21% had no medical coverage, median disease duration was 6 months (25th, 75th percentiles 4, 9 months), median HAQ DI score was 1.25 (25th, 75th percentiles 0.63, 2.00), median DAS28-ESR score was 6.2 (25th, 75th percentiles 4.9, 7.2), and 25% had erosions. Women and Mestizos, African LA, and Amerindians had earlier onset than men or Caucasians (P < 0.01). When adjusted by country, the analysis of covariance model showed that low/low-middle SES, female sex, partial coverage, and older age were associated with worse HAQ DI scores; only low/low-middle SES was associated with higher DAS28 scores. Statistically significant differences were found in HAQ DI and DAS28 scores between countries. When excluding country, low/low-middle SES, female sex, and no coverage were associated with worse HAQ DI and DAS28 scores, whereas separated/divorced/widowed status was associated with worse HAQ DI scores and age was associated with worse DAS28 scores. Logistic regression showed that older age, no coverage, and the Amerindian and other ethnic groups were associated with erosions. Conclusion. We compared early RA patients from the main LA ethnic groups. Our findings suggest that low/low-middle SES is important in determining disease activity. A more genetic-related background for erosions is possible.

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Purpose. To assess the impact of a six-month stage-based intervention on fruit and vegetable intake, regarding perceived benefits and barriers, and self-efficacy among adolescents. Design. Randomized treatment-control, pre-post design. Subjects/ Setting. Schools were randomized between control and experimental groups. 860 adolescents from ten public schools in Bras ' ilia, Federal District, Brazil were evaluated at baseline; 771 (81%) completed the study. Intervention. Experimental group received monthly magazines and newsletters aimed at promotion of healthy eating. Measures. Self-reported fruit and vegetable intake, stages of change, self-efficacy and decisional balance scores were evaluated at baseline and post-intervention in both groups. Analysis. The effectiveness of the intervention was evaluated using the analysis of covariance model (ANCOVA) and repeated measurement analysis by means of weighted least squares. Comparison between the proportions of adolescents who advanced through the stages during the intervention was performed using the Mantel-Haenszel chi-square test. Results. After adjusting for sex and age, study variables showed no modifications through the proposed intervention. There was no statistical difference in participant mobility in the intervention and control groups between the stages of change, throughout the study. Conclusion. A nutritional intervention based exclusively on distribution of stage-matched printed educational materials was insufficient to change adolescents' dietary behavior.

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OBJECTIVE: To compare the effect of bimatoprost and the fixed combination of latanoprost and timolol (LTFC) on 24-hour mean intraocular pressure (IOP) after patients are switched from a nonfixed combination of latanoprost and timolol. DESIGN: Randomized, double-masked, multicenter clinical trial. PARTICIPANTS: Two hundred patients with glaucoma or ocular hypertension. METHODS: Included were patients who were controlled (IOP < 21 mmHg) on the nonfixed combination of latanoprost and timolol for at least 3 months before the baseline visit or patients on monotherapy with either latanoprost or timolol who were eligible for dual therapy not being fully controlled on monotherapy. The latter group of patients underwent a 6-week wash-in phase with the nonfixed combination of latanoprost and timolol before baseline IOP determination and study inclusion. Supine and sitting position IOPs were recorded at 8 pm, midnight, 5 am, 8 am, noon, and 4 pm at baseline, week 6, and week 12 visits. MAIN OUTCOME MEASURE: An analysis of covariance model was used for a noninferiority test of the primary efficacy variable, with mean area under the 24-hour IOP curve after 12 weeks of treatment as response variable and treatment, center, and baseline IOP as factors. A secondary analysis was performed on the within-treatment change from baseline. RESULTS: Mean baseline IOPs were 16.3+/-3.3 mmHg and 15.5+/-2.9.mmHg in the bimatoprost and LTFC groups, respectively. At week 12, mean IOPs were 16.1+/-2.5 mmHg for the bimatoprost group and 16.3+/-3.7 mmHg for the LTFC group, and no significant difference between the 2 treatment groups could be found. As compared with baseline, mean IOP increased by 0.3+/-3.6 mmHg during the day and decreased by 0.8+/-3.8 mmHg during the night in the bimatoprost group, whereas there were increases of 1.43+/-2.6 mmHg and 0.14+/-3.2 mmHg in the LTFC group, respectively. CONCLUSIONS: Bimatoprost is not inferior to the LTFC in maintaining IOP at a controlled level during a 24-hour period in patients switched from the nonfixed combination of latanoprost and timolol.

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PURPOSE: To evaluate the relationship between ocular perfusion pressure and color Doppler measurements in patients with glaucoma. MATERIALS AND METHODS: Twenty patients with primary open-angle glaucoma with visual field deterioration in spite of an intraocular pressure lowered below 21 mm Hg, 20 age-matched patients with glaucoma with stable visual fields, and 20 age-matched healthy controls were recruited. After a 20-minute rest in a supine position, intraocular pressure and color Doppler measurements parameters of the ophthalmic artery and the central retinal artery were obtained. Correlations between mean ocular perfusion pressure and color Doppler measurements parameters were determined. RESULTS: Patients with glaucoma showed a higher intraocular pressure (P <.0008) and a lower mean ocular perfusion pressure (P <.0045) compared with healthy subjects. Patients with deteriorating glaucoma showed a lower mean blood pressure (P =.033) and a lower end diastolic velocity in the central retinal artery (P =.0093) compared with normals. Mean ocular perfusion pressure correlated positively with end diastolic velocity in the ophthalmic artery (R = 0.66, P =.002) and central retinal artery (R = 0.74, P <.0001) and negatively with resistivity index in the ophthalmic artery (R = -0.70, P =.001) and central retinal artery (R = -0.62, P =.003) in patients with deteriorating glaucoma. Such correlations did not occur in patients with glaucoma with stable visual fields or in normal subjects. The correlations were statistically significantly different between the study groups (parallelism of regression lines in an analysis of covariance model) for end diastolic velocity (P =.001) and resistivity index (P =.0001) in the ophthalmic artery, as well as for end diastolic velocity (P =.0009) and resistivity index (P =. 001) in the central retinal artery. CONCLUSIONS: The present findings suggest that alterations in ocular blood flow regulation may contribute to the progression in glaucomatous damage.

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Background: Management of type 2 diabetes with metformin often does not provide adequate glycemic control, thereby necessitating add-on treatment. In a 24-week clinical trial, dapagliflozin, an investigational sodium glucose cotransporter 2 inhibitor, improved glycemic control in patients inadequately controlled with metformin. The present study is an extension that was undertaken to evaluate dapagliflozin as long-term therapy in this population.Methods: This was a long-term extension (total 102 weeks) of a 24-week phase 3, multicenter, randomized, placebo-controlled, double-blind, parallel-group trial. Patients were randomly assigned (1:1:1:1) to blinded daily treatment (placebo, or dapagliflozin 2.5 to 5, or 10 mg) plus open-label metformin (=1,500 mg). The previously published primary endpoint was change from baseline in glycated hemoglobin (HbA1c) at 24 weeks. This paper reports the follow-up to week 102, with analysis of covariance model performed at 24 weeks with last observation carried forward; a repeated measures analysis was utilized to evaluate changes from baseline in HbA1c, fasting plasma glucose (FPG), and weight.Results: A total of 546 patients were randomized to 1 of the 4 treatments. The completion rate for the 78-week double-blind extension period was lower for the placebo group (63.5%) than for the dapagliflozin groups (68.3% to 79.8%). At week 102, mean changes from baseline HbA1c (8.06%) were +0.02% for placebo compared with -0.48% (P = 0.0008), -0.58% (P

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This dissertation analyzes whether and how changes in federal tax policy affect local tax policies, specifically, the elimination of the federal deductibility of state and local taxes for individual taxpayers by the Tax Reform Act of 1986 (TRA86) in 59 California cities. Two methods are used in the study: a survey of local revenue officials and a time event time-series/cross sectional sales tax reliance study.^ The reliance study uses a covariance model to pool cross-section and time-series observations. The results of the reliance study indicate a statistically significant overall decline in sales tax reliance after 1986. The results of the survey indicate that local policy makers generally do not believe that federal deductibility is an important factor when considering raising local sales taxes. Further analysis shows that local revenue officials claiming federal deductibility is not an important factor are associated mostly with cities that registered no significant decline in sales tax reliance after 1986. Similarly, local revenue officials claiming federal deductibility is an important factor when considering local tax policy are associated mostly with cities that suffered a significant decline in sales tax reliance after 1986.^ Of that group, further analysis shows that the declines in sales tax reliance are associated mostly with cities located in the southwestern part of the state. When compared to other cities in the state, an analysis of variance reveals that there are a series of statistically significant factors associated with southwestern cities which may contribute to the decline in sales tax reliance following the enactment of the Tax Reform Act of 1986. ^

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The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model). The matrix exponential transformation guarantees the positivedefiniteness of the dynamic covariance matrix. The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of econometric models”, Journal of Econometrics, 1988, 39(1-2), 69–104), especially for developing tests for leverage and spillover effects in the covariance dynamics. Efficient importance sampling is used to maximize the likelihood function of RMESV-ALM, and the finite sample properties of the quasi-maximum likelihood estimator of the parameters are analysed. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The forecasting performance of the new model is compared with a novel dynamic realized matrix-exponential conditional covariance model. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions from returns to volatility and co-volatility.