This paper establishes a hybrid distributed phased array multiple-input multiple-output (PA-MIMO) radar system model to improve the target detection performance by combining coherent processing gain and spatial diversity gain. First, signal space configuration for PA-MIMO are established. Then, novel likelihood ratio test (LRT) detector is derived based on Neyman–Pearson (NP) criterion in fixed...

Value-at-risk (VaR) and conditional value-at-risk (CVaR) are popular risk measures from academic, industrial and regulatory perspectives. The problem of minimizing CVaR is theoretically known to be of a Neyman–Pearson type binary solution. We add a constraint on expected return to investigate the mean-CVaR portfolio selection problem in a dynamic setting: the investor is faced with a Markowitz ...

Under regularity assumptions, we establish a sharp large deviation principle for Hermitian quadratic forms of stationary Gaussian processes. Our result is similar to the well-known Bahadur-Rao theorem [2] on the sample mean. We also provide several examples of application such as the sharp large deviation properties of the Neyman-Pearson likelihood ratio test, of the sum of squares, of the Yule...

Pearson (1897) investigated correlations of ratios of bone measurements and found that although the correlations among the original measures were low, the correlations among ratios with common measures were about .5. To understand this result, he developed an approximate equation for the correlations of ratios. In the present study, Monte Carlo methods were used to show that Pearson's equation ...

These are some notes on a very simple comparative introduction to four basic approaches of statistical inference—Fisher, Neyman–Pearson, Fisher/Neyman–Pearson hybrid, and Bayes—from a course on Quantitative & Statistical Reasoning at OU in Fall 2016. In particular, I hope to give a rough understanding of the differences between the frequentist and Bayesian paradigms, though they are not entirel...

We investigate the performance of the Neyman-Pearson detection of a stationary Gaussian process in noise, using a large wireless sensor network (WSN). In our model, each sensor compresses its observation sequence using a linear precoder. The final decision is taken by a fusion center (FC) based on the compressed information. Two families of precoders are studied: random iid precoders and orthog...

We consider a simple hypothesis and alternative about an abstract random observation parametrized by A from a directed set A. The asymptotics over A is evaluated for mixed errors of the Bayes tests and second kind errors of the Neyman-Pearson tests. Similar asymptotics has been first evaluated by H. ChernofT and Ch. Stein when A is the set of naturals and the observation consists of the first X...

In the most common formulation of the binary hypothesis distributed detection problem, local detectors collect statistically independent and identically distributed observations under each hypothesis. Based upon these data, each detector independently decides which hypothesis it judges to be true and then transmits its decision to a fusion center. The fusion center in turn makes a final global ...