Results 211 to 220 of about 355,518 (271)
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Journal of Mathematical Sciences, 2022
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Theory of Probability & Its Applications, 1989
See the review in Zbl 0662.60038.
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See the review in Zbl 0662.60038.
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Theory of Probability & Its Applications, 1989
See the review in Zbl 0656.60042.
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See the review in Zbl 0656.60042.
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Theory of Probability & Its Applications, 2000
Summary: The structure of the nonuniform estimate of the convergence rate in the local central limit theorem for the densities of sums of independent identically distributed random variables is made more accurate. The absolute constants are written out explicitly.
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Summary: The structure of the nonuniform estimate of the convergence rate in the local central limit theorem for the densities of sums of independent identically distributed random variables is made more accurate. The absolute constants are written out explicitly.
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On approximation and estimation of distribution function of sum of independent random variables
Statistical Papers, 2023N. N. Midhu +3 more
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Density Function of Weighted Sum of Chi-Square Variables with Doubly Degenerate Weights
Optical Memory and Neural Networks, 2022B. Kryzhanovsky, V. I. Egorov
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Lithuanian Mathematical Journal, 2012
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1991
This paper develops a new approach (called the Censored Centered Method, or CCM) to the problem of determining centers and scales for distributions, and features applications to asymptotic normality for censored sums of independent, generally nonidentically distributed random variables.
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This paper develops a new approach (called the Censored Centered Method, or CCM) to the problem of determining centers and scales for distributions, and features applications to asymptotic normality for censored sums of independent, generally nonidentically distributed random variables.
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1975
The results of this section, which are of interest in themselves, will play an important role in Chapters V and VI.
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The results of this section, which are of interest in themselves, will play an important role in Chapters V and VI.
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Theory of Probability & Its Applications, 2013
For the uniform distance $\Delta_n$ between the distribution function of the standard normal law and the distribution function of the normalized sum of $n$ independent random variables $X_1,\ldots,X_n$ with symmetric distribution functions $F_1,\ldots,F_n$ and ${\mathbf E}\,|X_j|=\beta_{1,j}$, ${\mathbf E}\,X_j^2=\sigma_j^2$, ${j=1,\ldots,n}$, for all $
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For the uniform distance $\Delta_n$ between the distribution function of the standard normal law and the distribution function of the normalized sum of $n$ independent random variables $X_1,\ldots,X_n$ with symmetric distribution functions $F_1,\ldots,F_n$ and ${\mathbf E}\,|X_j|=\beta_{1,j}$, ${\mathbf E}\,X_j^2=\sigma_j^2$, ${j=1,\ldots,n}$, for all $
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