Results 101 to 110 of about 90,888 (192)

Firm size and economic concentration: An analysis from a lognormal expansion. [PDF]

PLoS One, 2021
Cortés LM, Lozada JM, Perote J.
europepmc   +1 more source

EDGEWORTH CORRECTION FOR THE LARGEST EIGENVALUE IN A SPIKED PCA MODEL. [PDF]

Stat Sin, 2018
Yang J, Johnstone IM.
europepmc   +1 more source

Software-Illustrated Explanations of Econometrics Contributions by CR Rao for his 100-th Birthday. [PDF]

J Quant Econ, 2020
Vinod HD.
europepmc   +1 more source

Higher Order Properties of the Wild Bootstrap Under Misspecification [PDF]

We examine the higher order properties of the wild bootstrap (Wu, 1986) in a linear regression model with stochastic regressors. We find that the ability of the wild bootstrap to provide a higher order refinement is contingent upon whether the errors are
Andres Santos, Patrick M. Kline
core  

Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets. [PDF]

PLoS One, 2022
Hakim A, Salman ANM, Ashari Y, Syuhada K.   +3 more
europepmc   +1 more source

Asymptotic Expansions for Some Semiparametric Program Evaluation Estimators [PDF]

We investigate the performance of a class of semiparametric estimators of the treatment effect via asymptotic expansions. We derive approximations to the first two moments of the estimator that are valid to 'second order'.
Oliver Linton, Hidehiko Ichimura
core  

Multivariate edgeworth expansions

Journal of Computational and Applied Mathematics
Christopher S. Withers, Saralees Nadarajah   +1 more
openaire   +2 more sources

Computer Algebra and Algorithms for Unbiased Moment Estimation of Arbitrary Order. [PDF]

Cogent Math Stat, 2019
Gerlovina I, Hubbard AE.
europepmc   +1 more source

Semiparametric maximum likelihood probability density estimation. [PDF]

PLoS One, 2021
Kwasniok F.
europepmc   +1 more source

Bandwidth Selection in Nonparametric Kernel Testing [PDF]

We propose a sound approach to bandwidth selection in nonparametric kernel testing. The main idea is to find an Edgeworth expansion of the asymptotic distribution of the test concerned.
Jiti Gao, Irene Gijbels
core