Comparisons and Analogies for Non-parametric Problems
DRAFT- Analogies to Succeed: Applications to Service Design Problems
... After performing an ANOVA, an overall statistically significant difference between the total number of novel ideas generated is found between the conditions (F=8.06, p-value=0.001). Tukey’s pairwise comparisons ...
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Parametric methods outperformed non-parametric methods in comparisons of discrete numerical variables
... using parametric methods for confidence intervals and hypothesis tests ...a non-parametric test and a non- parametric or bootstrap confidence interval, or a trans- formation, for ...
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On Parametric (and Non-Parametric) Variation
... This brief characterization raises a number of problems. The first of these is the issue of deciding which phenomena are to be accounted for by reference to principles and which by reference to parameters, as ...
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International comparisons of the technical efficiency of the hospital sector: Panel data analysis of OECD countries using parametric and non-parametric approaches
... with parametric and nonparametric techniques across a number of models using aggregate hospital data at country level and controlling for the differences in case sever- ...
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Characterization of the stability set for non differentiable fuzzy parametric optimization problems
... This note gives the characterization of the stability set of the first kind for convex multiobjective nonlinear programming (MONLP) problems with fuzzy parameters in the constraints and for convex MONLP ...
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The Practice of Non Parametric Estimation by Solving Inverse Problems: The Example of Transformation Models
... dY = 0 and ϕ = constant. This hypothesis is false is Z + U is constant which is an extreme dependence between Z and the noise U. More generally it is su cient that Z + U may vary indepen- dently of W to verify the ...
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No theory: an explanation of the lack of consistency in cross-country health care comparisons using non-parametric estimators
... well-known problems that make validity and inference a problem. The problems include the DEA estimator having less than root-n convergence due to the curse of dimensionality, where the number of ...
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Parametric Comparisons of Classification Techniques in Data Mining Applications
... Data Mining (DM)(knowledge Discovery in databases) is the process of extraction of interesting(non-trivial, implicit, previously unknown and potentially useful) pattern or information from large databases using ...
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Parametric and non-parametric analysis of tax changes
... Despite the magnitude of our data set we need to acknowledge several limitations. Specif- ically, while our data set includes complete location descriptors for each sale and a descriptor on land use code which identifies ...
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Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems
... defined non-parametric probability models and whether the related finite-dimensional estimators have well-behaving limits; this motivates the study of Bayesian inverse problems in the infinite- ...
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Non-Parametric Spatial Models
... of parametric covariance functions guarantee that the fitted covariance function is positive ...two non-parametric approaches to modelling the covariance ...by problems that arise in spatial ...
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Non Parametric Instrumental Regression
... between parametric and nonparametric rates of ...inverse problems introduces in Econometrics a different albeit related class of concepts of regularity of ...
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The Parametric and Non-parametric Bootstrap Resamplings for the Visual Acuity Measurement
... the problems of the regression model we can refer to Habing [6], Hossain et ...the parametric and non-parametric bootstrap for variance estimation depended on the sample kurtosis and on the ...
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Selecting the W Matrix: Parametric vs. Non Parametric Approaches
... δ 0 V ˆ −1 ˆ δ ∼ χ 2 (2N ), (9) being ˆ V the estimated sample covariance of ˆ δ. Burridge and Fingleton (2010) show that the asymptotic Chi-square distribution can be a poor approximation. They advocate for a bootstrap ...
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Comparison of Parametric (OLS) and Non-Parametric (THEIL’S) Linear Regression
... Ohlson and Kim (2014) carried a research on Linear Valuation without OLS: The Theil-Sen Estimation Approach. According to them, OLS confronts two well-known problems in many archival accounting research settings. ...
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Semi Parametric Estimation of Long Memory: Comparisons and Some Attractive Alternatives
... to non stationary processes. Non-stationary long memory process are still amenable to impulse response analy- sis, since when ...to non-stationary long memory processes is provided by the following ...
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On parametric implicit vector variational inequality problems
... lower semi-continuity of the solution of PIVEP in locally convex Hausdorff topological vector spaces. This paper is motivated and inspired by the recent paper [] and its aim is to extend the results to the setting of ...
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Parametric and non-parametric forest biomass estimation from PolInSAR data
... After the ground-volume separation and parameter re- trieval, AGB is regressed through LR, SVM and RF. LR is solved by least squares. SVM and RF are based on ma- chine learning classification approaches, adapted to ...
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Parametric Linear Complementarity Problems
... Remember that in 7] ve types of generalized critical points of one-parametric nonlinear optimization problems described by C 3-functions have been identied to be generic, whereas the pap[r] ...
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Parametric and Bayesian non-parametric estimation of copulas.
... the parametric family of the Archimedean ...the parametric assumption and present Bayesian techniques to estimate the joint density and the generator of an Archimedean copula ...
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