卷 234, 编号 6 (2018)

Since data for statistical analysis are always given in a discretized form, observations contain not only measurement errors but also rounding errors which are determined by the discretization step. In this paper we consider situations where the rounding errors are considerable: they are comparable to or even greater (in average) than the measurement errors. It is shown that it can be reasonable to increase the measurement errors in order to reduce the error of the final result.

In this paper we consider the problem of inverting linear homogeneous transforms by Vaguelette–Wavelet decomposition and stabilized hard thresholding of noisy wavelet coefficients. We also prove asymptotic normality and strong consistency of the mean-square risk estimate for this method.

The well-known problem of finding explicit formulas for the expected return and risk of portfolios with general commission is completely solved. It is assumed that the commission depends on the asset and the asset position, and on whether the given position is opened or closed. For portfolios with only the budget constraint and initial commission, we prove that the function of expected portfolio return and portfolio variance function are bounded.

In this paper we consider the problem of estimation of a signal function from the noised observations via thresholding its wavelet coefficients. We find the asymptotic order of the optimal threshold that minimizes the probability of the maximum error between the estimates and the true wavelet coefficients exceeding a critical value.

A rate of convergence in the CLT is studied for recurrent Markov chains. The Berry–Esseen type bound is obtained with explicit dependence on parameters of the chain. The method of the proof is purely analytical. It is based on the complex analysis of generating functions.

This paper is a further development of the results of [20] where, based on the negative binomial model for the duration of wet periods measured in days [16], an asymptotic approximation was proposed for the distribution of the maximum daily precipitation volume within a wet period. This approximation has the form of a scale mixture of the Fr´echet distribution with the gamma mixing distribution and coincides with the distribution of a positive power of a random variable having the Snedecor–Fisher distribution. Here we extend this result to the mth extremes, m ∈ ℕ, and sample quantiles. The proof of this result is based on the representation of the negative binomial distribution as a mixed geometric (and hence, mixed Poisson) distribution [17] and limit theorems for extreme order statistics in samples with random sizes having mixed Poisson distributions [10]. Some analytic properties of the obtained limit distribution are described. In particular, it is demonstrated that under certain conditions the limit distribution of the maximum precipitation is mixed exponential and hence, is infinitely divisible. It is shown that under the same conditions this limit distribution can be represented as a scale mixture of stable or Weibull or Pareto or folded normal laws. The corresponding product representations for the limit random variable can be used for its computer simulation. The results of fitting this distribution to real data are presented illustrating high adequacy of the proposed model. It is also shown that the limit distribution of sample quantiles is the well-known Student distribution. Several methods are proposed for the estimation of the parameters of the asymptotic distributions. The obtained mixture representations for the limit laws and the corresponding asymptotic approximations provide better insight into the nature of mixed probability (“Bayesian”) models.