The Pearson-Fisher chi-squared test can be used to evaluate the goodnessof-fit of categorized continuous data with known bin endpoints compared to a continuous distribution, in the presence of unknown (nuisance) distribution parameters. Rayner and McAlevey [11] and Rayner and Best [9],[10] demonstrate that in this case, component tests of the Pearson-Fisher chi-squared test statistic can be obt...

• This paper presents a pure likelihood approach to statistical inference and its relation to diagnostic tests in clinical medicine. The standard antagonistic frequentist approaches of Fisher and Neyman–Pearson–Wald, and the Bayesian perspective are discussed. We advocate that in medicine, usually, the likelihood is the only source of information available. Also, it is shown that there is no di...

This paper presents a generalized likelihood ratio technique for detection of defect locations from bobbin coil eddy current data. First a Neyman±Pearson (NP) decision rule for detection of known random signals (in presence of noise) is discussed. The result is then generalized to the problem of detection of unknown random signals that are commonly found in bobbin coil eddy current data. The pe...

We consider the problem of decentralized detection in a sensor network consisting of nodes arranged as a tree of bounded height. We characterize the optimal error exponent under a Neyman-Pearson formulation, and show that the Type II error probability decays exponentially with the number of nodes. Surprisingly, the optimal error exponent is often the same as that corresponding to a parallel con...

Frequentist statistics is often applied in the framework of null hypothesis significance testing (NHST). We will look at the Neyman-Pearson paradigm which focuses on one hypothesis called the null hypothesis. There are other paradigms for hypothesis testing, but NeymanPearson is the most common. Stated simply, this method asks if the data is well outside the region where we would expect to see ...

Spectrum sensing is important both in searching spectrum hole for secondary user access and protecting the primary users from interference. This paper proposes Energy based Sequence Detection for fast spectrum sensing. Different from the traditional fixed-slot energy detection, our scheme senses the spectrum sequentially using the Neyman-Pearson test until it collects enough statistics for a de...

Inability to clearly defend against the criticisms of frequentist methods has turned many a frequentist away from venturing into foundational battlegrounds. Conceding the distorted perspectives drawn from overly literal and radical expositions of what Fisher, Neyman, and Pearson ‘really thought’, some deny they matter to current practice. The goal of this paper is not merely to call attention t...

In this paper, we propose a goodness-of-fit test based on the empirical likelihood method for the generalized lambda distribution (GLD) family. Such a nonparametric test approximates the optimal Neyman-Pearson likelihood ratio test under the unknown alternative distribution scenario. The p-value of the test is approximated through the simulations due to the dependency of the test statistic on t...

We derive a large deviation result for the log-likelihood ratio for testing simple hypotheses in locally stationaryGaussian processes. This result allows us to find explicitly the rates of exponential decay of the error probabilities of type I and type II for Neyman–Pearson tests. Furthermore, we obtain the analogue of classical results on asymptotic efficiency of tests such as Stein’s lemma an...

Abstract– In MIMO (Multiple Input Multiple Output) radar systems multiple antennas at transmit and receive sides are used to improve the detection performance. Two important parameters which have an effect on the coverage and performance of these systems are: the transmitters’ power and the receivers’ positions. In this paper, assuming the Rayleigh scatter model for the target, the probability ...