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A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorre...
Although advanced statistical models have been proposed to fit complex data better, the advances of science and technology have generated more complex data, e.g., Big Data, in which existing probabili...
The classical chi-square test for independence in a two-way contingency table often rejects the independence hypothesis at an extremely small signif- icance level, particularly when the sample siz...
Consider the decentralized estimation of an unknown parameter by bandwidth constrained sensor network with a fusion center. Local sensors make observations which are linearly scaled versions of these ...
Abstract: We introduce kernel estimators for the semicircle law. In this first part of our general theory on the estimators, we prove the consistency and conduct simulation study to show the performan...
The main result of this note is Theorem 7 below. The main interest is the array of new Bellman function, very different from Burkholder’s function.
We scale and analyze the empirical data of return from New York and Vilnius stock exchanges matching it to the same nonlinear double stochastic model of return in financial market.
We prove that the convolution of a selfdecomposable dis-tribution with its background driving law is again selfdecomposable if and only if the background driving law is s-selfdecomposable.

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