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In this paper we analyze some properties of the empirical diagonal and we obtain its exact distribution under independence for the two and three- dimensional cases, but the ideas proposed in this paper can be carried out to higher dimensions. The results obtained are useful in designing a nonparametric test for independence, and therefore giving solution to an open problem proposed by Alsina, Frank and Schweizer [2].
In this paper it is shown how one can work out exact distributions of estimators and test statistics in the model of a regression line for the threshold parameter with exponential distribution of errors. This is done on a test statistics which is related to a problem of Zvára [Zvara95].
Integral representations of the exact distributions of order statistics are derived in a geometric way when three or four random variables depend on each other as the components of continuous ln,psymmetrically distributed random vectors do, n ∈ {3,4}, p > 0. Once the representations are implemented in a computer program, it is easy to change the density generator of the ln,p-symmetric distribution with another one for newly evaluating the distribution of interest. For two groups of stock exchange...
Consider independent and identically distributed random variables {X nk, 1 ≤ k ≤ m, n ≤ 1} from the Pareto distribution. We select two order statistics from each row, X n(i) ≤ X n(j), for 1 ≤ i < j ≤ = m. Then we test to see whether or not Laws of Large Numbers with nonzero limits exist for weighted sums of the random variables R ij = X n(j)/X n(i).
The failure time distribution for various items often follows a shifted (two-parameter) exponential model and not the traditional (one-parameter) exponential model. The shifted exponential is very useful in practice, in particular in the engineering, biomedical sciences and industrial quality control when modeling time to event or survival data. The open problem of simultaneous testing for differences in origin and scale parameters of two shifted exponential distributions is addressed. Two exact...
The author studies the linear rank statistics for testing the pypothesis of randomness against the alternative of two samples provided both are drawn grom discrete (integer-valued) distributions. The weak law of large numbers and the exact slope are obtained for statistics with randomized ranks of with averaged scores.
The paper deals with sufficient conditions for the existence of general approximate minimum distance estimator (AMDE) of a probability density function on the real line. It shows that the AMDE always exists when the bounded -divergence, Kolmogorov, Lévy, Cramér, or discrepancy distance is used. Consequently, consistency rate in any bounded -divergence is established for Kolmogorov, Lévy, and discrepancy estimators under the condition that the degree of variations of the corresponding family...
R-ε criterion is considered in a decision problem (Θ, D*, R). Some considerations are made for the case in which the parameter space Θ is finite. Finally the existence of a decision rule with the minimum R-ε risk is examined, when the risk set is closed from below and bounded.
We extend Leibniz' rule for repeated derivatives of a product to multivariate integrals of a product.
As an application we obtain expansions for P(a < Y < b) for Y ~ Np(0,V) and
for repeated integrals of the density of Y.
When V−1y > 0 in R3 the expansion for P(Y < y) reduces to
one given by [H. Ruben J. Res. Nat. Bureau Stand. B 68 (1964) 3–11]. in terms of the moments of Np(0,V−1).
This is shown to be a special case of an expansion in terms of the multivariate Hermite polynomials.
These...
We extend Leibniz' rule for repeated derivatives of a product to multivariate integrals of a product. As an application we obtain expansions for P(a < Y < b) for Y ~ Np(0,V) and for repeated integrals of the density of Y. When V−1y > 0 in R3 the expansion for P(Y < y) reduces to one given by [H. Ruben J. Res. Nat. Bureau Stand. B 68 (1964) 3–11]. in terms of the moments of Np(0,V−1). This is shown to be a special case of an expansion in terms of the multivariate Hermite...
We give a stochastic expansion for estimates that minimise the arithmetic mean of (typically independent) random functions of a known parameterθ. Examples include least squares estimates, maximum likelihood estimates and more generally M-estimates. This is used to obtain leading cumulant coefficients of needed for the Edgeworth expansions for the distribution and densityn1/2θ0) to magnitude n−3/2 (or to n−2 for the symmetric case), where θ0 is the true parameter value and n is typically the...
We give a stochastic expansion for estimates
that minimise the arithmetic mean of (typically independent) random functions of a known parameter θ.
Examples include least squares estimates, maximum likelihood estimates and more generally M-estimates.
This is used to obtain leading cumulant coefficients of
needed for the Edgeworth expansions for the distribution and density n1/2 (
of − θ0) to magnitude n−3/2 (or to n−2 for the symmetric case),
where θ0 is the true parameter value and n is typically...
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