Expressions for "-entropy rate, "-mutual information rate and "-divergence rate are introduced. These quantities, which consist of the quantiles of the asymptotic information spectra, generalize the inf/sup-entropy/information/divergence rates of Han and Verd u. The algebraic properties of these information measures are rigorously analyzed, and examples illustrating their use in the computation...

We connect the power of Confidence Intervals in different Frequentist methods to their reliability. We show that in the case of a bounded parameter a biased method which near the boundary has large power in testing the parameter against larger alternatives and small power in testing the parameter against smaller alternatives is desirable. Considering the recently proposed methods with correct c...

Null hypothesis significance testing has been under attack in recent years, partly owing to the arbitrary nature of setting α (the decision-making threshold and probability of Type I error) at a constant value, usually 0.05. If the goal of null hypothesis testing is to present conclusions in which we have the highest possible confidence, then the only logical decision-making threshold is the va...

In this paper, the application of a type-II hybrid ARQ protocol in a slotted direct-sequence spread-spectrum multiple-access (DS-SSMA) packet radio system is investigated. Both the static performance and the dynamic performance of such a system are analyzed. In the physical layer, packet error and packet success probabilities are computed using the improved Gaussian approximation technique, whi...

We introduce tests for finite-sample linear regressions with heteroskedastic errors. The tests are exact, i.e., they have guaranteed type I error probabilities when bounds are known on the range of the dependent variable, without any assumptions about the noise structure. We provide upper bounds on probability of type II errors, and apply the tests to empirical data.

Power is defined as the probability of correctly detecting an effect. It is often noted , where the converse, ̄ is the probability of a type-II error (not rejecting H0 when there is an effect). When planning a new experiment, it is generally recommended to have a power of at least .80. Suppose that your design has very little power and suppose further that you found a significant effect. Since y...