Strong approximations of the Q-Q process
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Publication:1821422
DOI10.1016/0047-259X(86)90022-9zbMath0616.60033MaRDI QIDQ1821422
Publication date: 1986
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Brownian bridgestrong approximationempirical distribution functionKiefer processGlivenko-Cantelli-type results
Nonparametric tolerance and confidence regions (62G15) Order statistics; empirical distribution functions (62G30) Strong limit theorems (60F15)
Related Items (11)
Strong approximations for weighted bootstrap of empirical and quantile processes with applications ⋮ The complex behaviour of Galton rank-order statistic ⋮ On quantile processes for m-dependent Rv's ⋮ Approximations and two-sample tests based on P-P and Q-Q plots of the Kaplan-Meier estimators of lifetime distributions ⋮ Strong approximation of multidimensional \(\mathbb P\)-\(\mathbb P\) plots processes by Gaussian processes with applications to statistical tests ⋮ A modified one-sample Q-Q plot and a test for normality ⋮ Local polynomial fitting of the equipercentile equating function: strong uniform consistency ⋮ On the approximation of P-P and Q-Q plot processes by Brownian bridges ⋮ Quantile-quantile plots under random censorship ⋮ Some Nonparametric Tests for Change-Point Detection Based on the ℙ-ℙ and ℚ-ℚ Plot Processes ⋮ Hodges-Lehmann quantile-quantile plots
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- Strong approximations of the quantile process
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- An approximation of partial sums of independent RV'-s, and the sample DF. I
- Confidence procedures for two-sample problems
- The law of the iterated logarithm for normalized empirical distribution function
- Plotting with confidence: Graphical comparisons of two populations
- Limit theorems for the ratio of the empirical distribution function to the true distribution function
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