[PDF] Top 20 An edgeworth expansion for a sum of M Dependent random variables
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An edgeworth expansion for a sum of M Dependent random variables
... WAN SOO RHEE Faculty of Management Sciences The Ohio State University Columbus, Ohio 43210.. of m-dependent random variables with..[r] ... See full document
7
On the characteristic function of a sum of M dependent random variables
... RHEE Faculty of Management Sciences The Ohio State University Columbus, Ohio 43210... We will estimate the bound of.[r] ... See full document
8
Comparing Risk Neutral Density Estimation Methods using Simulated Option Data
... that Edgeworth expansion method provides incontestably the best statistical perfor- mance with an acceptance rate of 100%: for each of the 500 observations, the density estimated using this method agrees ... See full document
9
The Almost Sure Convergence for Weighted Sums of Linear Negatively Dependent Random Variables
... among random variables isn't ...some random variables are often related to decreases in other random variables and the assumption of negative dependence is more appropriate than ... See full document
6
On the strong convergence for weighted sums of negatively superadditive dependent random variables
... In this paper, we use different methods from those of Sung [] and Chen and Sung [] to prove the results, and we obtain some strong convergence results for weighted sums of NSD random variables without the ... See full document
14
Precise large deviations for widely orthant dependent random variables with different distributions
... Heyde [3, 4], Heyde [5], Mikosch and Nagaev [6], Nagaev [7], Nagaev [8], Ng et al. [9] and so on. In Paulauskas and Skučait˙e [10] and Skučait˙e [11], the precise large deviations of a sum of independent but not ... See full document
18
On Strong Law of Large Numbers for Dependent Random Variables
... Since the definition of complete convergence was introduced by Hsu and Robins, there have been many authors who devote themselves to the study of the complete convergence for sums of independent and dependent RVs ... See full document
13
Mathematical models of geometric sizes of coffee beans as dependent random variables
... the sum of squared deviations (SSD) is used in order to compare the results of the approximation of experimental data on the measurement of bean grain sizes by two-dimensional distributions of their geometric ... See full document
6
Complete convergence for negatively orthant dependent random variables
... In this paper, necessary and sufficient conditions of the complete convergence are obtained for the maximum partial sums of negatively orthant dependent (NOD) random variables. The results extend and ... See full document
12
ON THE LAWS OF LARGE NUMBERS FOR DEPENDENT RANDOM VARIABLES
... = ∑ X n = S n ( ) / n . Landers and Rogge [8] proved a strong law of large numbers (SLLN) for pairwise independent and strongly uniformly integrable r.v.’s. Chandra and Goswami [3] proved a more general SLLN for pairwise ... See full document
5
On the complete convergence for pairwise negatively quadrant dependent random variables
... Robbins [] proved that the sequence of arithmetic means of independent and identi- cally distributed (i.i.d.) random variables converges completely to the expected value if the variance of the summands is ... See full document
11
A note on the complete convergence for weighted sums of negatively dependent random variables
... In (.), a ≈ b means that a = O(b) and b = O(a). Theorem . extends the result of Liang and Su [] for negatively associated random variables to negatively dependent case. The proof of the ... See full document
10
Strong Laws for Weighted Sums of Negative Dependent Random Variables
... Since the conception of PND sequences contains independent and negatively associated sequences, which have a lot of applications, e.g. in reliability theory, Percolation theory and multivariate statistical analysis, ... See full document
6
Maximal Inequalities for Dependent Random Variables and Applications
... In this paper, we give a sufficient condition under which 1.2 and 1.3 hold. Our results partially improve those of Hu et al. 1, 2. The technique used in our proof is the well- known method of subsequences. Note that the ... See full document
10
A Strong Limit Theorem for Weighted Sums of Sequences of Negatively Dependent Random Variables
... ND random variables, the notions of ND dependence of random variables have received more and more attention ...NA random variables to the case of ND variables is highly ... See full document
8
Cauchy approximation for sums of independent random variables
... [19] C. Stein, A bound for the error in the normal approximation to the distribution of a sum of dependent random variables, Proceedings of the Sixth Berkeley Symposium on Mathematical ... See full document
12
Asymptotic tail probability of weighted infinite sum of conditionally dependent and consistently varying tailed random variables
... This paper will mainly focus on the asymptotic behavior of the tail probability of a weighted infinite sum of heavy-tailed r.v.s under the above two extended conditional de- pendence structures. In the rest of this ... See full document
15
Complete moment convergence for weighted sums of negatively superadditive dependent random variables
... NSD random variables with EX i r < ∞ for all i ≥ and some r > ...NSD random variables and gave its applications to nonparametric regression ...NSD random variables and ... See full document
13
Equivalent conditions of complete moment convergence for extended negatively dependent random variables
... dom variables. Our results not only extend and generalize some results on the complete moment convergence such as obtained by Chow ( []) and Li and Spătaru ( []) from the i.i.d. case to extended ... See full document
11
Wavelets for Nonparametric Stochastic Regression with Pairwise Negative Quadrant Dependent Random Variables
... This definition was introduced by Lehmann (1966). Suppose that { , is a sequence of pairwise NQD random variables with a common one-dimensio- nal marginal probability density function f. The problem of ... See full document
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