Approximations to Distributions

2012 ◽  
Keyword(s):  
Approximations To Distributions

General saddlepoint approximations to distributions under an elliptical population

1999 ◽  
Vol 28 (3-4) ◽  
pp. 727-754 ◽  
Author(s):  
Takahiro Tsuchiya ◽  
Sadanori Konishi
Keyword(s):  
Saddlepoint Approximations ◽  
Approximations To Distributions

Improved approximations to distributions of the largest and the smallest latent roots of a wishart matrix

1981 ◽  
Vol 33 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Sadanori Konishi ◽  
Takakazu Sugiyama
Keyword(s):  
Wishart Matrix ◽  
Approximations To Distributions ◽  
Latent Roots

Approximations to Distributions

2004 ◽  
Author(s):  
K. O. Bowman ◽  
L. R. Shenton
Keyword(s):  
Approximations To Distributions

scholarly journals Bootstrap Methods in Econometrics

2019 ◽  
Vol 11 (1) ◽  
pp. 193-224 ◽  
Author(s):  
Joel L. Horowitz
Keyword(s):  
Asymptotic Distribution ◽  
Confidence Intervals ◽  
Test Statistic ◽  
Bootstrap Methods ◽  
First Order ◽  
Coverage Probabilities ◽  
Asymptotic Distribution Theory ◽  
Nominal Coverage ◽  
Distributional Approximations ◽  
Approximations To Distributions

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in the references that are cited.

Numerical Approximations to Distributions of Weighted Kolmogorov-Smirnov Statistics via Integral Equations

2019 ◽  
pp. 579-589
Author(s):  
Dan Wu ◽  
Lin Yee Hin ◽  
Nino Kordzakhia ◽  
Alexander Novikov
Keyword(s):  
Integral Equations ◽  
Numerical Approximations ◽  
Kolmogorov Smirnov ◽  
Approximations To Distributions

scholarly journals On the accuracy of bootstrapping sample quantiles of strongly mixing sequences

2007 ◽  
Vol 82 (2) ◽  
pp. 263-282 ◽  
Author(s):  
Shuxia Sun
Keyword(s):  
Rate Of Convergence ◽  
Marginal Distribution ◽  
Regularity Conditions ◽  
One Dimensional ◽  
Strongly Mixing ◽  
Moving Block Bootstrap ◽  
Mixing Sequences ◽  
The One ◽  
Approximations To Distributions ◽  
Sample Quantiles

AbstractIn this paper, we examine the rate of convergence of moving block bootstrap (MBB) approximations to the distributions of normalized sample quantiles based on strongly mixing observations. Under suitable smoothness and regularity conditions on the one-dimensional marginal distribution function, the rate of convergence of the MBB approximations to distributions of centered and scaled sample quantiles is of order O(n−1¼ log logn).

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