A central limit theorem for random fields.
A central limit theorem for triangular arrays of weakly dependent random variables, with applications in statistics
We derive a central limit theorem for triangular arrays of possibly nonstationary random variables satisfying a condition of weak dependence in the sense of Doukhan and Louhichi [Stoch. Proc. Appl. 84 (1999) 313–342]. The proof uses a new variant of the Lindeberg method: the behavior of the partial sums is compared to that of partial sums of dependent Gaussian random variables. We also discuss a few applications in statistics which show that our central limit theorem is tailor-made for statistics...
A chain of inequalities for some types of multivariate distributions, with nine special cases
A Chain Ratio-Type Estimator in Finite Population Double Sampling Using Two Auxiliary Variables.
A chain regression estimator in two phase sampling using multi-auxiliary information.
A Characterization Of Cramér Representation Of Stochastic Processes
A characterization of Cramér representation of stochastic processes.
A Characterization of Exponential Distributions Based on Order Statistics.
A characterization of matrix variate normal distribution.
A Characterization of Sufficiency by Power Functions.
A Characterization of the Binomial and Negative Binomial Distribution by Regression of Products.
A Characterization of the Exponential Distribution by Higher Gap.
A Characterization of the Expontential Distribution
A characterization of the gamma distribution in terms of conditional moment
A characteriyation of the Gamma distribution in terms of the -th conditional moment presented in this paper extends the result of Shunji Osaki and Xin-xiang Li (1988).
A Characterization of the Gamma Process by Conditional Moments.
A Characterization of the Normal Distribution by Sufficiency of the Least Squares Estimation.
A Characterization of Uniform Distribution
Is the Lebesgue measure on [0,1]² a unique product measure on [0,1]² which is transformed again into a product measure on [0,1]² by the mapping ψ(x,y) = (x,(x+y)mod 1))? Here a somewhat stronger version of this problem in a probabilistic framework is answered. It is shown that for independent and identically distributed random variables X and Y constancy of the conditional expectations of X+Y-I(X+Y > 1) and its square given X identifies uniform distribution either absolutely continuous or discrete....
A Characterization using the Conditional Variance.
A choice of criterion parameters in a linearization of regression models.
A Class of Non Binary Unequally Replicated E-Optimal Designs.