75 resultados para truncated regression


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In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

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Aims. Diabetes mellitus is a growing health problem worldwide. This study aimed to describe dysglycaemia and determine the impact of body composition and clinical and lifestyle factors on the risk of progression or regression from impaired fasting glucose (IFG) to diabetes or normoglycaemia in Australian women. Methods. This study included 1167 women, aged 20-94 years, enrolled in the Geelong Osteoporosis Study. Multivariable logistic regression was used to identify predictors for progression to diabetes or regression to normoglycaemia (from IFG), over 10 years of follow-up. Results. At baseline the proportion of women with IFG was 33.8% and 6.5% had diabetes. Those with fasting dysglycaemia had higher obesity-related factors, lower serum HDL cholesterol, and lower physical activity. Over a decade, the incidence of progression from IFG to diabetes was 18.1 per 1,000 person-years (95% CI, 10.7-28.2). Fasting plasma glucose and serum triglycerides were important factors in both progression to diabetes and regression to normoglycaemia. Conclusions. Our results show a transitional process; those with IFG had risk factors intermediate to normoglycaemics and those with diabetes. This investigation may help target interventions to those with IFG at high risk of progression to diabetes and thereby prevent cases of diabetes.

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Abstract
Purpose– The purpose of this paper is to estimate the determinants of the retail space rent in Shanghai.
Design/methodology/approach – Hedonic model and spatial regression models are used in the paper. The problem of spatial autocorrelation is tested by Moran’s I statistics, and the root mean square error (RMSE) test is performed to find out the best model.
Findings – The significant explaining variables are the age, the area of retail space, the distance to the Jing An CBD centre, the type of the retail and the district of the property. A new classification of district in retail research context is suggested in this paper, and it is proved to be better than the districts set up by government to explain the retail rent variation.
Originality/value – This paper presents the first empirical study about the retail rental market in Shanghai. The research helps retail property investors and retail tenants deepen their understanding of the retail market in Shanghai. Spatial econometrics techniques are first introduced into the empirical retail rent research to produce a more precise estimation.

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Background

Previous reviews on risk and protective factors for violence in psychosis have produced contrasting findings. There is therefore a need to clarify the direction and strength of association of risk and protective factors for violent outcomes in individuals with psychosis.

Method

We conducted a systematic review and meta-analysis using 6 electronic databases (CINAHL, EBSCO, EMBASE, Global Health, PsycINFO, PUBMED) and Google Scholar. Studies were identified that reported factors associated with violence in adults diagnosed, using DSM or ICD criteria, with schizophrenia and other psychoses. We considered non-English language studies and dissertations. Risk and protective factors were meta-analysed if reported in three or more primary studies. Meta-regression examined sources of heterogeneity. A novel meta-epidemiological approach was used to group similar risk factors into one of 10 domains. Sub-group analyses were then used to investigate whether risk domains differed for studies reporting severe violence (rather than aggression or hostility) and studies based in inpatient (rather than outpatient) settings.

Findings

There were 110 eligible studies reporting on 45,533 individuals, 8,439 (18.5%) of whom were violent. A total of 39,995 (87.8%) were diagnosed with schizophrenia, 209 (0.4%) were diagnosed with bipolar disorder, and 5,329 (11.8%) were diagnosed with other psychoses. Dynamic (or modifiable) risk factors included hostile behaviour, recent drug misuse, non-adherence with psychological therapies (p values<0.001), higher poor impulse control scores, recent substance misuse, recent alcohol misuse (p values<0.01), and non-adherence with medication (p value <0.05). We also examined a number of static factors, the strongest of which were criminal history factors. When restricting outcomes to severe violence, these associations did not change materially. In studies investigating inpatient violence, associations differed in strength but not direction.

Conclusion

Certain dynamic risk factors are strongly associated with increased violence risk in individuals with psychosis and their role in risk assessment and management warrants further examination.

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Regression is at the cornerstone of statistical analysis. Multilevel regression, on the other hand, receives little research attention, though it is prevalent in economics, biostatistics and healthcare to name a few. We present a Bayesian nonparametric framework for multilevel regression where individuals including observations and outcomes are organized into groups. Furthermore, our approach exploits additional group-specific context observations, we use Dirichlet Process with product-space base measure in a nested structure to model group-level context distribution and the regression distribution to accommodate the multilevel structure of the data. The proposed model simultaneously partitions groups into cluster and perform regression. We provide collapsed Gibbs sampler for posterior inference. We perform extensive experiments on econometric panel data and healthcare longitudinal data to demonstrate the effectiveness of the proposed model

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This paper presents the first comprehensive synthesis of economic valuations of wetlands in developing countries. Meta-regression analysis (MRA) is applied to 1432 estimates of the economic value of 379 distinct wetlands from 50 countries. We find that wetlands are a normal good, wetland size has a negative effect on wetland values, and urban wetlands and marine wetlands are more valuable than other wetlands. Wetland values estimated by stated preferences are lower than those estimated by market price methods. The MRA benefit transfer function has a median transfer error of 17%. Overall, MRA appears to be useful for deriving the economic value of wetlands at policy sites in developing nations.

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Karnik-Mendel (KM) algorithm is the most widely used type reduction (TR) method in literature for the design of interval type-2 fuzzy logic systems (IT2FLS). Its iterative nature for finding left and right switch points is its Achilles heel. Despite a decade of research, none of the alternative TR methods offer uncertainty measures equivalent to KM algorithm. This paper takes a data-driven approach to tackle the computational burden of this algorithm while keeping its key features. We propose a regression method to approximate left and right switch points found by KM algorithm. Approximator only uses the firing intervals, rnles centroids, and FLS strnctural features as inputs. Once training is done, it can precisely approximate the left and right switch points through basic vector multiplications. Comprehensive simulation results demonstrate that the approximation accuracy for a wide variety of FLSs is 100%. Flexibility, ease of implementation, and speed are other features of the proposed method.

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The reliable and efficient design of steel-fibre-reinforced concrete (SFRC) structures requires clear knowledge of material properties. Since the locations and orientations of aggregates and fibres in concrete are intrinsically random, testing results from different specimens vary, and it needs hundreds or even thousands of specimens and tests to derive the unbiased statistical distributions of material properties by using traditional statistical techniques. Therefore, few statistical studies on the SFRC material properties can be found in literature. In this study, high-rate impact test results on SFRC using split Hopkinson pressure bar are further analysed. The influences of different strain rates and various volume fractions of fibres on compressive strength of SFRC specimens under dynamic loadings will be quantified, by using kernel regression, a kernel-based nonparametric statistical method. Several kernel estimators and functions will be compared. This technique allows one to derive an unbiased statistical estimation from limited testing data. Therefore it is especially useful when the testing data is limited.

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Support for the saying “a picture is worth a 1000 words’ has been consistently found within statistics education. Graphical images are effective in promoting understanding and communication of statistical concepts and results to a variety of audiences. The computer software package, AMOS, was developed for the analysis of structural equation models (SEM) and has a user-friendly graphical interface. However, courses in SEM are generally found only at the postgraduate level. This paper argues that the graphical interface of AMOS has the potential to enhance conceptual understanding and communication of results in undergraduate statistical courses. More specifically, approaches to the teaching and communication of results of multiple regression models when using SPSS and AMOS will be examined and compared.

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A growing literature documents the existence of strategic political reactions to publicexpenditure between rival jurisdictions. These interactions can potentially createa downward expenditure spiral (“race to the bottom”) or a rising expenditure spiral(“race to the top”). However, in the course of identifying the existence of such interactions and ascertaining their underlying triggers, the empirical evidence has produced markedly heterogeneous findings. Most of this heterogeneity can be traced back to study design and institutional differences. This article contributes to the literature by applying meta-regression analysis to quantify the magnitude of strategic inter-jurisdictional expenditure interactions, controlling for study, and institutional characteristics. We find several robust results beyond confirming that jurisdictions do engage in strategic expenditure interactions, namely that strategic interactions: (i) are weakening over time, (ii) are stronger among municipalities than among higher levels of government, and (iii) appear to be more influenced from tax competition than yardstick competition, with capital controls and fiscal decentralization shaping the magnitude of fiscal interactions.

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This article examines the empirical support for the hypothesized hedonic theoretical relation between the price of wine and its quality. The examination considers over 180 hedonic wine price models developed over 20 years, covering many countries. The research identifies that the relation between the price of wine and its sensory quality rating is a moderate partial correlation of +0.30. This correlation exists despite the lack of information held by consumers about a wine’s quality and the inconsistency of expert tasters when evaluating wines. The results identify a moderate price-quality correlation, which suggests the existence of strategic buying opportunities for better informed consumers. Strategic price setting possibilities may also exist for wine producers given the incomplete quality information held by consumers. The results from the meta-regression analysis point to the absence of any publication bias, and attribute the observed asymmetry in estimates to study heterogeneity. The analysis suggests the observed heterogeneity is explained by the importance of a wine’s reputation, the use of the 100-point quality rating scale, the analysis of a single wine variety/style, and the employed functional form. The most important implication from the analysis is the relative importance of a wine’s reputation over its sensory quality, inferring that producers need to sustain the sensory quality of a wine over time to extract appropriate returns. The reputation of the wine producer is found not to influence the strength of the price quality relationship. This finding does not contradict the importance of wine producer reputation in directly influencing prices.

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Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of‘mixed-effects’ or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the ‘true’ regression coefficient.

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When the distribution of a process characterized by a profile is non normal, process capability analysis using normal assumption often leads to erroneous interpretations of the process performance. Profile monitoring is a relatively new set of techniques in quality control that is used in situations where the state of product or process is represented by a function of two or more quality characteristics. Such profiles can be modeled using linear or nonlinear regression models. In some applications, it is assumed that the quality characteristics follow a normal distribution; however, in certain applications this assumption may fail to hold and may yield misleading results. In this article, we consider process capability analysis of non normal linear profiles. We investigate and compare five methods to estimate non normal process capability index (PCI) in profiles. In three of the methods, an estimation of the cumulative distribution function (cdf) of the process is required to analyze process capability in profiles. In order to estimate cdf of the process, we use a Burr XII distribution as well as empirical distributions. However, the resulted PCI with estimating cdf of the process is sometimes far from its true value. So, here we apply artificial neural network with supervised learning which allows the estimation of PCIs in profiles without the need to estimate cdf of the process. Box-Cox transformation technique is also developed to deal with non normal situations. Finally, a comparison study is performed through the simulation of Gamma, Weibull, Lognormal, Beta and student-t data.

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Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation anti-virus engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current anti-virus engines in detecting malware. In this paper, we propose a stepwise binary logistic regression-based dimensionality reduction techniques for malware detection using application program interface (API) call statistics. Finding the most significant malware feature using traditional wrapper-based approaches takes an exponential complexity of the dimension (m) of the dataset with a brute-force search strategies and order of (m-1) complexity with a backward elimination filter heuristics. The novelty of the proposed approach is that it finds the worst case computational complexity which is less than order of (m-1). The proposed approach uses multi-linear regression and the p-value of each individual API feature for selection of the most uncorrelated and significant features in order to reduce the dimensionality of the large malware data and to ensure the absence of multi-collinearity. The stepwise logistic regression approach is then employed to test the significance of the individual malware feature based on their corresponding Wald statistic and to construct the binary decision the model. When the selected most significant APIs are used in a decision rule generation systems, this approach not only reduces the tree size but also improves classification performance. Exhaustive experiments on a large malware data set show that the proposed approach clearly exceeds the existing standard decision rule, support vector machine-based template approach with complete data and provides a better statistical fitness.