Results 21 to 30 of about 11,729 (266)

Moment Multicalibration for Uncertainty Estimation

open access: yesCoRR, 2020
We show how to achieve the notion of "multicalibration" from Hébert-Johnson et al. [2018] not just for means, but also for variances and other higher moments. Informally, it means that we can find regression functions which, given a data point, can make point predictions not just for the expectation of its label, but for higher moments of its label ...
Christopher Jung 0001   +4 more
openaire   +3 more sources

Moment Estimation and Dithered Quantization [PDF]

open access: yesIEEE Signal Processing Letters, 2006
This letter examines the influence of low-bit quantization on moment estimators with special emphasis on the 1-bit case. Moment estimators are especially useful if no prior knowledge on the distribution of the observations is available or if an ML approach is analytically intractable or computationally infeasible.
Stefan Geirhofer   +2 more
openaire   +1 more source

Application of the Heavy-tailed Estimation in Financial Data

open access: yesJournal of Harbin University of Science and Technology, 2019
There exist many marginal distributions of high frequency time series data in the Heavy-tailed distribution which stores a great deal of information in its tail.
CHEN Hai-long, HUANG Fei, XIE Sheng
doaj   +1 more source

Improved Average Estimation in Seemingly Unrelated Regressions

open access: yesEconometrics, 2020
In this paper, we propose an efficient weighted average estimator in Seemingly Unrelated Regressions. This average estimator shrinks a generalized least squares (GLS) estimator towards a restricted GLS estimator, where the restrictions represent possible
Ali Mehrabani, Aman Ullah
doaj   +1 more source

Rapidly Adapting Moment Estimation

open access: yesCoRR, 2019
11 ...
Guoqiang Zhang 0003   +2 more
openaire   +2 more sources

A New Type of Moment Estimator for the K-distribution Shape Parameter with High Accuracy and Efficiency

open access: yesLeida xuebao, 2014
The X-Estimator (XE) for the K-distribution shape parameter v based on the zlog(z) expectation can be computed without solving nonlinear equations; thus, it has high estimating efficiency.
Li Da-peng
doaj   +1 more source

Measuring the Sensitivity of Parameter Estimates to Estimation Moments* [PDF]

open access: yesThe Quarterly Journal of Economics, 2017
AbstractWe propose a local measure of the relationship between parameter estimates and the moments of the data they depend on. Our measure can be computed at negligible cost even for complex structural models. We argue that reporting this measure can increase the transparency of structural estimates, making it easier for readers to predict the way ...
Gentzkow, Matthew   +2 more
openaire   +3 more sources

A note on the bootstrap method for testing the existence of finite moments

open access: yesStatistica, 2014
This paper discusses a bootstrap-based test, which checks if finite moments exist, and indicates cases of possible misapplication. It notes, that a procedure for finding the smallest power to which observations need to be raised, such that the test ...
Igor Fedotenkov
doaj   +1 more source

The Statistical Curvature of Seemingly Unrelated Unrestricted Regression Equations. [PDF]

open access: yesThe Egyptian Statistical Journal, 1997
We study the finite sample properties of an asymptotically efficient estimator for coefficients of seemingly unrelated unrestricted regression (SUUR) equations. Zellner (1963) derived the exact probability density function of the SUUR estimator.
Ahmed Youssef
doaj   +1 more source

Estimation of the Shape Parameter of Ged Distribution for a Small Sample Size

open access: yesFolia Oeconomica Stetinensia, 2014
In this paper a new method of estimating the shape parameter of generalized error distribution (GED), called ‘approximated moment method’, was proposed. The following estimators were considered: the one obtained through the maximum likelihood method (MLM)
Purczyński Jan   +1 more
doaj   +1 more source

Home - About - Disclaimer - Privacy