Non-parametric Kernel Estimation with Real Data
Index tracking model, downside risk and non-parametric kernel estimation
... sample data to define tracking er- ror, we treat tracking error as a random variable and adopt its higher-order moment to define ...sample data to estimate tracking ...
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Parametric and non-parametric forest biomass estimation from PolInSAR data
... for non-parametric and non-linear classification and ...input data into a higher-dimensional feature space, where the problem can be addressed in lin- earized ...(RBF) kernel is used ...
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Non-Asymptotic Kernel-based Parametric Estimation of Continuous-time Linear Systems
... Since 2001 he is also Danieli Endowed Chair of Automation Engineering with University of Trieste. In 2009-2012 he was Deputy Rector of University of Trieste. He authored or co-authored more than 250 research papers in ...
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Parametric and Bayesian non-parametric estimation of copulas.
... the data in terms of either averaging an MCMC sample as we do in chapter 4, or using the raw empirical ...spline estimation method does not deteriorate in performance as fast as its alternative when the ...
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An Adaptive Non-parametric Kernel Method for Classification
... DeVeaux, Psichogios and Ungar [23] report that the MARS technique was more accu- rate and faster than neural nets, but that its accuracy degraded badly with noise. Splines are generally used to smooth examples of a ...
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Kernel Estimation in Line Transect Sampling for Parametric Model
... For this purpose, we used HR model to generate 400 samples of sizes š = 50, 100 and š = 200 of perpendicular distances data set. Four HR models is selected with parameter values š½ and the corresponding truncated ...
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Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression
... Much prepayment research has been done in the private sector rather than for purely academic purposes. Wall Street investment banks, major lenders, the GSEs and others have developed numerous econometric models that ...
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Bayesian non-parametric conditional copula estimation of twin data
... twins data set, where the lower trian- gular panels represent pairwise scatterplots, the upper triangular panels show pairwise Pearson correlation coefficients and the diagonal panels represent the histograms of ...
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Bayesian non-parametric conditional copula estimation of twin data
... pseudo-observations with respect to the motherās level of education; panels (c) and (d): scatterplots of the predictive and transformed predictive samples; panels (e) and (f): histograms of the real data ...
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A Family of Simple Non-Parametric Kernel Learning Algorithms
... of Non-Parametric Kernel Learning (NPKL) usually formulate the learning task as a Semi-Definite Programming (SDP) problem that is often solved by some general purpose SDP ...N data examples, ...
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Non-parametric estimation of ROC curve
... SP method is fast, but slower than BB method. BN-G method is also very slow. From the simulation results, the BN-G method gives rise to large IAE for the estimate of the ROC curve and the BN-T method seems to be very ...
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Non-parametric and semi-parametric estimation of spatial covariance function
... large data sets on spheres has drawn attention ...and estimation, it is often necessary to have a good estimate of the covariance structure of the underlying ...of parametric models and face the ...
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Kernel Density Estimation for Heaped Data
... for kernel density estimation for heaped data was ...realistic non-parametric density estimates and gives additional insights into the rounding ...validation data solely on the ...
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Non-parametric Parameter Estimation and Clinical Data Fitting with a Model of HIV Infection
... the data for all 118 patients in the data set, including the clinical identification number assigned to the patient, number of longitudinal vi- ral load and CD4 + measurements, the total length of time from ...
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Economic Growth and Environmental Quality: A Non-Parametric Kernel. Estimation of the Environmental Kuznets Curve * Savas Alpay and Syed Mahmud
... Our estimation methodology differs with Grossman and Krueger [1995] in two key ...employ non-parametric kernel estimation technique to establish the nature of income-pollution ...
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Non-parametric estimation of the coefficients of ergodic diffusion processes based on high-frequency data
... proposes non-parametric estimators using projec- tions on wavelet bases together with adaptive ...studying non-parametric estimation in this framework and ...
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Fault Prognostics Using Logical Analysis of Data and Non-Parametric Reliability Estimation Methods
... vibration data collected from a real application in the industry. The data contain a number of time waveform signals collected from a set of bearings subjected to one of many failure ...
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Floating Car and Camera Data Fusion for Non-parametric Route Travel Time Estimation
... two data sources in the estimation of arterial route travel ...eļ¬cient, non-parametric method for route travel time estimation using both ANPR data and low-frequency ...the ...
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Kernel density estimation with doubly truncated data
... truncated data were explored in ...given parametric family, was investigated in [9], see also ...knowledge, estimation of a density function observed under random double truncation has not been ...
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A non Parametric Estimation of Service Level in a Discrete Context
... alternative non parametric approach so that these issues are ...the estimation of the service level considering: (i) demand distribution selection and validation using sample data even when ...
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