Talk:Estimator - Factbites

Talk:Estimator - Factbites
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Topic: Talk:Estimator

Note: these results are not from the primary (high quality) database.

	ion-grama.text
		The aim of the present talk is to give a natural resolution to the "Hill horror plot" paradox and to "rehabilitate" the Hill estimator, by looking at the problem from the point of view of selecting an appropriate Pareto type tail.
		It is now commonly recognized that for finite samples sizes the Hill estimator does not accurately estimate the quantity it was designed to estimate, the index of regular variation.
		It turns out that, for finite sample sizes, the Hill estimator is closer to another quantity, which can be interpreted as the fitted Pareto index.
	math.ucsd.edu /~politis/CONTR/ion-grama.text (194 words)

	Seminars and Events - Research Colloquia
		In this talk I will describe a method to stabilize the harmonic mean estimator.
		While this is a simulation-consistent estimator, it can have infinite variance.
		On the Harmonic Mean Estimator of Marginal Probability
	www.stat.purdue.edu /calendar/seminars/research_colloquia/spring-01/col-jaya20010118.html (153 words)

	Department of Statistics
		In this talk we shall explore why the accuracy of the estimator deteriorates if the underlying process is too smooth, and we propose a family of new estimators which do have the desired accuracy.
		The graph of a Gaussian stochastic process, regarded as a subset of the plane, has a fractal dimension lying between 1 and 2.
		The fractal dimension depends on the underlying smoothness of the process, with smoother processes having a lower dimension.
	www.amsta.leeds.ac.uk /Statistics/seminars/kent.html (153 words)

	PlanetMath: variance
		However, as this measure is squared, the standard deviation is used instead when one wants to talk about how much a random variable varies around its expected value.
		Cross-references: estimator, linear combination, covariance, relation, satisfies, standard deviation, measure, mean, variation, level, moment, formula, function, random variable
		This is version 6 of variance, born on 2001-10-26, modified 2002-08-26.
	planetmath.org /encyclopedia/Variance.html (143 words)

	ann02
		In this talk, I will propose a general entropy-based method for testing of standard exponential families.
		The test statistic can take different forms, depending on the type of entropy estimator.
	math.bu.edu /research/statistics/colloq/99/ann02 (122 words)

	Abstract: Statistics Dept Seminars
		Similar to the Chi-square distributions they are used to describe the distribution of the estimator of the covariance matrix in a multivariate normal model.
		The talk will discuss such an extension and its use in multivariate statistical analysis, in particular in graphical models.
		They generalize the Chi-square distributions from distributions on the positive real line to distributions on the cones of positive definite matrices of different dimemsions.
	www.yale.edu /stat/Seminars/1999-00/andersson.html (122 words)

	Sonderforschungsbereich 475 - Newsletter
		Holger Dette (A2, B1), "A Simple Nonparametric Estimator of a Monotone Regression Function ", Invited talk, International Seminar on Nonparametric Inference (ISNI), A Coru ñ a, Spain, July 13-15, 2005.
		Holger Dette (A2, B1), Lorens A. Imhof, "Uniform Approximation of Eigenvalues in Laguerre and Hermite β-Ensembles by Roots of orthogonal Polynomials".
		Holger Dette (A2, B1), Kay Pilz (A2), "On the Estimation of a Monotone Conditional Variance in Nonparametric Regression".
	www.sfb475.uni-dortmund.de /dienst/de/textonly/content/Newsletter-d/Newsletter-d.html (122 words)

	Seminar on Scaling
		In this talk we explore statistical properties of estimators of MF spectra base on the discrete WT for the case of fractional Brownian motion (fBm).
		We also present exact asymptotic bounds for the minimax mean square risks of the estimators.
		We present a new estimator for the spectral measure and show that it is asymptotically normal.
	math.bu.edu /people/murad/colloq-01-02.html (122 words)

	simonoff.html
		This talk describes a classical smoothing parameter selector based on an improved version of AIC (termed AIC_C) that can be derived for any linear estimator and does not exhibit the high variability and tendency to undersmoothing of GCV or AIC.
		Classical methods such as generalized cross-validation (GCV) and the Akaike information criterion (AIC) have to some extent fallen into a state of disuse because of two unfavorable properties: the selectors lead to highly variable choices of smoothing parameter, and they possess a noticeable tendency towards undersmoothing.
		Plug-in methods, in contrast, are based on deriving the bandwidth that minimizes the asymptotic weighted conditional mean integrated squared error, and then substituting estimates for the unknown values in that bandwidth.
	www.yale.edu /stat/Seminars/1997-98/simonoff.html (122 words)

	mease.txt
		Boosted Classification Trees, Overfitting, and Conditional Class Probability Estimation Abstract: In this talk we will examine some issues relating to boosting.
		The results suggest that when viewed as an estimator of conditional class probabilities, boosting necessarily overfits.
		In contrast, when viewed as an approximation to the optimal Bayes classifier, overfitting rarely occurs and surprisingly the boosting algorithms benefit from using fairly strong base learners and a large number of iterations.
	www.stat.berkeley.edu /seminars/Neymanf04/mease.txt (213 words)

	Ron Williams Construction: Insurance Survival Guide
		WHO TO TALK TO You may be noticing that the one person (your estimator) you were dealing with seems to have turned into a team of professionals to assist you in every way possible.
		Different departments of Ron Williams Construction are now handling the different aspects of the repairs to your property.
		When the check arrives, please treat it as another payment to Ron Williams Construction Company for repairs to your property.
	www.firerestoration.com /guide.html (213 words)

	3/11/98 Susan Murphy
		This talk concerns the verification of the intuitive practice of profiling the nuisance parameter out of the likelihood and using the resulting profile likelihood as if it is a likelihood for the vector parameter.
		That is, will maximizing the profile likelihood yield an asymptotically normal estimator of the parameter of interest?
		In high dimensional models, interest often lies primarily in a vector parameter and the nuisance parameter is a function, such as a distribution function or a smooth regression function.
	www.cs.jhu.edu /~cowen/Seminars/murphy.html (213 words)

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