A nonparametric estimate of a multivariate density function
Problem solving - nonparametric estimate of probability density function
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Results for “nonparametric estimation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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On the nonparametric estimation of shift in the two-sample problem
Nonparametric estimation of shift in two sets of samples - one from each of two independent populations of independent elements
POPULATION↗
Nonparametric estimation of mean and variance when a few sample values possibly outliers
Nonparametric estimation of mean and variance in random sampling with observations of varying values
Walsh, J. E.↗
Nonparametric Bayer-risk estimation
Nonparametric Bayes risk estimation for measurement classification, using nearest neighbor error rate and Parzen probability density function estimators
Fralick, S. C.↗
A bound for the smoothing parameter in certain well-known nonparametric density estimators
Two classes of nonparametric density estimators, the histogram and the kernel estimator, both require a choice of smoothing parameter, or 'window width'. The optimum choice of this parameter is in general very difficult. An upper bound to the choices that depends only on the standard deviation of the distribution is described.
Terrell, G. R.↗
Characterization of a maximum-likelihood nonparametric density estimator of kernel type
Kernel type density estimators calculated by the method of sieves. Proofs are presented for the characterization theorem: Let x(1), x(2),...x(n) be a random sample from a population with density f(0). Let sigma 0 and consider estimators f of f(0) defined by (1).
Geman, S.↗
Advanced statistical methods for improved data analysis of NASA astrophysics missions
The investigators under this grant studied ways to improve the statistical analysis of astronomical data. They looked at existing techniques, the development of new techniques, and the production and distribution of specialized software to the astronomical community. Abstracts of nine papers that were produced are included, as well as brief descriptions of four software packages. The articles that are abstracted discuss analytical and Monte Carlo comparisons of six different linear least squares fits, a (second) paper on linear regression in astronomy, two reviews of public domain software for the astronomer, subsample and half-sample methods for estimating sampling distributions, a nonparametric estimation of survival functions under dependent competing risks, censoring in astronomical data due to nondetections, an astronomy survival analysis computer package called ASURV, and improving the statistical methodology of astronomical data analysis.
Feigelson, Eric D.↗
Nonparametric probability density estimation by optimization theoretic techniques
Two nonparametric probability density estimators are considered. The first is the kernel estimator. The problem of choosing the kernel scaling factor based solely on a random sample is addressed. An interactive mode is discussed and an algorithm proposed to choose the scaling factor automatically. The second nonparametric probability estimate uses penalty function techniques with the maximum likelihood criterion. A discrete maximum penalized likelihood estimator is proposed and is shown to be consistent in the mean square error. A numerical implementation technique for the discrete solution is discussed and examples displayed. An extensive simulation study compares the integrated mean square error of the discrete and kernel estimators. The robustness of the discrete estimator is demonstrated graphically.
Scott, D. W.↗
Nonparametric probability density estimation for data analysis in several dimensions
Nonparametric probability density estimates, in particular the corresponding contour curves, it is shown, are a useful adjunct to scatter diagrams when performing a preliminary examination of a set of random data in several dimensions.
Scott, D. W.↗
Nonparametric maximum likelihood estimation of probability densities by penalty function methods
When it is known a priori exactly to which finite dimensional manifold the probability density function gives rise to a set of samples, the parametric maximum likelihood estimation procedure leads to poor estimates and is unstable; while the nonparametric maximum likelihood procedure is undefined. A very general theory of maximum penalized likelihood estimation which should avoid many of these difficulties is presented. It is demonstrated that each reproducing kernel Hilbert space leads, in a very natural way, to a maximum penalized likelihood estimator and that a well-known class of reproducing kernel Hilbert spaces gives polynomial splines as the nonparametric maximum penalized likelihood estimates.
Demontricher, G. F.↗
Study of nonparametric techniques for estimating reliability and other life quality parameters Final report
Nonparametric techniques for probability distribution, probability density, and hazard function estimates for life quality
Murthy, V. K.↗
A nonparametric clustering technique which estimates the number of clusters
In applications of cluster analysis, one usually needs to determine the number of clusters, K, and the assignment of observations to each cluster. A clustering technique based on recursive application of a multivariate test of bimodality which automatically estimates both K and the cluster assignments is presented.
Ramey, D. B.↗
A validation methodology for fault-tolerant clock synchronization
A validation method for the synchronization subsystem of a fault-tolerant computer system is presented. The high reliability requirement of flight crucial systems precludes the use of most traditional validation methods. The method presented utilizes formal design proof to uncover design and coding errors and experimentation to validate the assumptions of the design proof. The experimental method is described and illustrated by validating an experimental implementation of the Software Implemented Fault Tolerance (SIFT) clock synchronization algorithm. The design proof of the algorithm defines the maximum skew between any two nonfaulty clocks in the system in terms of theoretical upper bounds on certain system parameters. The quantile to which each parameter must be estimated is determined by a combinatorial analysis of the system reliability. The parameters are measured by direct and indirect means, and upper bounds are estimated. A nonparametric method based on an asymptotic property of the tail of a distribution is used to estimate the upper bound of a critical system parameter. Although the proof process is very costly, it is extremely valuable when validating the crucial synchronization subsystem.
Johnson, S. C.↗
Topics in global convergence of density estimates
The problem of estimating a density f on R sup d from a sample Xz(1),...,X(n) of independent identically distributed random vectors is critically examined, and some recent results in the field are reviewed. The following statements are qualified: (1) For any sequence of density estimates f(n), any arbitrary slow rate of convergence to 0 is possible for E(integral/f(n)-fl); (2) In theoretical comparisons of density estimates, integral/f(n)-f/ should be used and not integral/f(n)-f/sup p, p 1; and (3) For most reasonable nonparametric density estimates, either there is convergence of integral/f(n)-f/ (and then the convergence is in the strongest possible sense for all f), or there is no convergence (even in the weakest possible sense for a single f). There is no intermediate situation.
Devroye, L.↗
Phase 1 of the earth resources data analysis program
Research completed in the Earth Resources Data Analysis Program is discussed along with recommendations for future study. Projects discussed include use of the Cholesky decomposition in feature selection and classification algorithms; optimal feature selection and extraction, probability density estimation and nonparametric classifiers; use of spatial information in classification; and model for crop row reflectance. The installation of LARSYS on the ICSA's IBM 370/155 is discussed, and a list of technical reports is included.
Source record↗
The X-ray luminosity function of very rich clusters and the luminosity-richness relation
For a sample of galactic clusters that includes richness class three, four, and five clusters, the significance of the luminosity-richness relation is estimated using nonparametric methods which are valid for any luminosity function. The Kolmogorov-Smirnov test is used to determine the significance at which the X-ray luminosities of clusters in one richness class are statistically equal to those in another. The a priori expectation that the high richness clusters are more luminous on average than lower richness objects is confirmed, but it is found that the luminosity function for clusters of richness class three or higher turns over for luminosities less than about 3 x 10 to the 44th ergs/s, while that for lower richness classes extends to at least an order of magnitude lower luminosity.
Soltan, A.↗
Permutation Testing and Bootstrapping: NonParametric Approaches to Statistical Testing and Estimation
No abstract available
Young, Millennia↗
On the estimation of climatological Z-R relationships
A statistical framework for climatological Z-R parameter estimation is developed and simulation experiments are conducted to examine sampling properties of the estimators. Both parametric and nonparametric models are considered. For parametric models, it is shown that Z-R parameters can be estimated by maximum likelihood, a procedure with optimal large sample properties. A general nonparametric framework for climatological Z-R estimation is also developed. Nonparametric procedures are attractive because of their flexibility in dealing with certain types of measurement errors common to radar data. Simulation experiments show that even under favorable assumptions on error characteristics of radar and raingages, large datasets are required to obtain accurate Z-R parameter estimates. Another important conclusion is that estimation results are generally quite sensitive to radar and raingage measurement thresholds. For fixed sample size, the simulation results can be used to provide quantitative assessments of the accuracy of Z-R model parameter estimates. These results are particularly useful for error analysis of precipitation products that are derived using climatological Z-R relations. One example is the large-area rainfall estimates derived using the height-area rainfall threshold (HART) technique.
Krajewski, Witold F.↗