On some two-step density estimation method.
On Special Case of Multiple Hypotheses Optimal Testing for Three Differently Distributed Random Variables
2000 Mathematics Subject Classification: 62P30.In this paper by using theory of large deviation techniques (LDT), the problem of hypotheses testing for three random variables having different distributions from three possible distributions is solved. Hypotheses identification for two objects having different distributions from two given probability distributions was examined by Ahlswewde and Haroutunian. We noticed Sanov's theorem and its applications in hypotheses testing.
On standard normal convergence of the multivariate Student -statistic for symmetric random vectors.
On stationarity of a multiple doubly stochastic model
On Statistical Inference in Concentration Measurement.
On stochastic comparisons of energy functions with applications.
On stochastic properties of past varentropy with applications
To have accuracy in the extracted information is the goal of the reliability theory investigation. In information theory, varentropy has recently been proposed to describe and measure the degree of information dispersion around entropy. Theoretical investigation on varentropy of past life has been initiated, however details on its stochastic properties are yet to be discovered. In this paper, we propose a novel stochastic order and introduce new classes of life distributions based on past varentropy....
On strong laws for generalized L-statistics with dependent data
It is pointed out that a strong law of large numbers for L-statistics established by van Zwet (1980) for i.i.d. sequences, remains valid for stationary ergodic data. When the underlying process is weakly Bernoulli, the result extends even to generalized L-statistics considered in Helmers et al. (1988).
On strong laws of large numbers for arrays of rowwise independent random elements.
On structural approximating multivariate discrete probability distributions
On Testing Exponentiality Against IFRA Alternatives.
On Testing for Independence Against Right Tail Increasing in Bivariate Models.
On Testing for Lorenz Ordering.
On testing hypotheses approximable by cones
On testing hypotheses in the generalized Skillings-Mack random blocks setting
The testing of the null hypothesis of no treatment effect against the alternative of increasing treatment effect by means of rank statistics is extended from the classical Friedman random blocks model into an unbalanced design allowing treatments not to be applied simultaneously in each random block. The asymptotic normality of the constructed rank test statistic is proved both in the setting not allowing ties and also for models with presence of ties. As a by-product of the proofs a multiple comparisons...
On testing of general random closed set model hypothesis
A new method of testing the random closed set model hypothesis (for example: the Boolean model hypothesis) for a stationary random closed set with values in the extended convex ring is introduced. The method is based on the summary statistics – normalized intrinsic volumes densities of the -parallel sets to . The estimated summary statistics are compared with theirs envelopes produced from simulations of the model given by the tested hypothesis. The p-level of the test is then computed via approximation...
On Testing the Correlation Coefficient of a Bivariate Normal Distribution.
On Testing the Dimensionality of Regression Coefficients.
On testing variance components in unbalanced mixed linear model
The paper presents some approximate and exact tests for testing variance components in general unbalanced mixed linear model. It extends the results presented by Seifert (1992) with emphasis on the computational aspects of the problem.
On the adaptive wavelet estimation of a multidimensional regression function under -mixing dependence: Beyond the standard assumptions on the noise
We investigate the estimation of a multidimensional regression function from observations of an -mixing process , where , represents the design and the noise. We concentrate on wavelet methods. In most papers considering this problem, either the proposed wavelet estimator is not adaptive (i.e., it depends on the knowledge of the smoothness of in its construction) or it is supposed that is bounded or/and has a known distribution. In this paper, we go far beyond this classical framework....