RFD-Rao and RFD-Wald tests for distributed targets with range walking effect

Two novel adaptive distributed target detectors, the range frequency domain-Rao （RFD-Rao） and range frequency domain-Wald （RFD-Wald） tests are proposed in this work. The application methods for these tests consider a partially homogeneous disturbance environment and a target range walking effect in a coherent processing interval （CPI）. The asymptotic performance of the detectors is analyzed, and the constant false alarm rate （CFAR） properties with respect to the clutter covariance matrix and power level are shown. The performances of the proposed adaptive detectors are assessed through Monte-Carlo simulations, and the results are presented to demonstrate the effectiveness of the proposed detection algorithms compared to those of similar existing detectors.

A Statistical Power Comparison of the Kolmogorov-Smirnov Two-Sample Test and the Wald Wolfowitz Test in Terms of Fixed Skewness and Fixed Kurtosis in Large Sample Sizes

In this study, the statistical powers of Kolmogorov-Smimov two-sample （KS-2） and Wald Wolfowitz （WW） tests, non-parametric tests used in testing data from two independent samples, have been compared in terms of fixed skewness and fixed kurtosis by means of Monte Carlo simulation. This comparison has been made when the ratio of variance is two as well as with equal and different sample sizes for large sample volumes. The sample used in the study is： （25, 25）, （25, 50）, （25, 75）, （25, 100）, （50, 25）, （50, 50）, （50, 75）, （50, 100）, （75, 25）, （75, 50）, （75, 75）, （75, 100）, （100, 25）, （100, 50）, （100, 75）, and （100, 100）. According to the results of the study, it has been observed that the statistical power of both tests decreases when the coefficient of kurtosis is held fixed and the coefficient of skewness is reduced while it increases when the coefficient of skewness is held fixed and the coefficient of kurtosis is reduced. When the ratio of skewness is reduced in the case of fixed kurtosis, the WW test is stronger in sample volumes （25, 25）, （25, 50）, （25, 75）, （25, 100）, （50, 75）, and （50, 100） while KS-2 test is stronger in other sample volumes. When the ratio of kurtosis is reduced in the case of fixed skewness, the statistical power of WW test is stronger in volume samples （25, 25）, （25, 75）, （25, 100）, and （75, 25） while KS-2 test is stronger in other sample volumes.

Hypothesis Testing of Population Percentiles via the Wald Test with Bootstrap Variance Estimates

Testing the equality of percentiles (quantiles) between populations is an effective method for robust, nonparametric comparison, especially when the distributions are asymmetric or irregularly shaped. Unlike global nonparametric tests for homogeneity such as the Kolmogorv-Smirnov test, testing the equality of a set of percentiles (i.e., a percentile profile) yields an estimate of the location and extent of the differences between the populations along the entire domain. The Wald test using bootstrap estimates of variance of the order statistics provides a unified method for hypothesis testing of functions of the population percentiles. Simulation studies are conducted to show performance of the method under various scenarios and to give suggestions on its use. Several examples are given to illustrate some useful applications to real data.

产业间信用风险的测算

文章从信用风险对制造业经营效率的影响出发,以Battese和Coelli的随机边界模型为基础、研究中国制造业在2002-2008年间的企业主要信用指标对经营绩效影响。实证结果表明,企业举债数量、应付利息和应收帐款周转期限都会影响经营效率;企业拥有充沛现金流将使信用风险降低,亦提高营运绩效。总体发现信用风险对中国制造业营运效率成反向关系。

Adaptive detector design of MIMO radar with unknown covariance matrix

The problem of detecting signal with multiple input mul-tiple output （MIMO） radar in correlated Gaussian clutter dominated scenario with unknown covariance matrix is dealt with. The gen-eral MIMO model, with widely separated sub-arrays and co-located antennas at each sub-array, is adopted. Firstly, the generalized likelihood ratio test （GLRT） with known covariance matrix is ob-tained, and then the Rao and Wald detectors are devised, which have proved that the Rao and Wald test coincide with GLRT detec-tor. To make the detectors fully adaptive, the secondary data with signal-free will be collected to estimate the covariance. The per-formance of the proposed detector is analyzed, however, it is just ancillary. A thorough performance assessment by several numer-ical examples is also given, which has considered the sense with co-located antennas configure of transmitters and receivers array. The results show that the performance the proposed adaptive de-tector is better than LJ-GLRT, and the loss can be acceptable in comparison to their non-adaptive counterparts.

Subspace detection for range-spread target to suppress interference:exploiting persymmetry in non-homogeneous scenario

This paper deals with subspace detection for rangespread target in non-homogeneous clutter with unknown covariance matrix where structured interference is presented in the received data.Through exploiting the persymmetry of the clutter covariance matrix,we propose two adaptive target detectors,which are referred to as persymmetric subspace Rao to suppress interference and persymmetric subspace Wald to suppress interference("PS-Rao-I"and"PS-Wald-I"),respectively.The persymmetry-based design brings in the advantage of easy implementation for small training sample support.The signal flow analysis of the two detectors shows that the PS-Rao-I rejects interference and integrates signals successively through separated matrix projection,while the PS-Wald-I jointly achieves interference elimination and signal combination via oblique projection.In addition,both detectors are shown to be constant false alarm rate detectors,significantly improving the detection performance with other competing detectors under the condition of limited training.

TESTING INEQUALITY CONSTRAINTS IN A LINEAR REGRESSION MODEL WITH SPHERICALLY SYMMETRIC DISTURBANCES

This paper develops a Wald statistic for testing the validity of multivariate inequality constraints in linear regression models with spherically symmetric disturbances,and derive the distributions of the test statistic under null and nonnull hypotheses.The power of the test is then discussed.Numerical evaluations are also carried out to examine the power performances of the test for the case in which errors follow a multivariate student-t(Mt) distribution.

Asymptotics on Semiparametric Analysis of Multivariate Failure Time Data Under the Additive Hazards Model

Many survival studies record the times to two or more distinct failures oneach subject. The failures may be events of different natures or may be repetitions of the same kindof event. In this article, we consider the regression analysis of such multivariate failure timedata under the additive hazards model. Simple weighted estimating functions for the regressionparameters are proposed, and asymptotic distribution theory of the resulting estimators are derived.In addition, a class of generalized Wald and generalized score statistics for hypothesis testingand model selection are presented, and the asymptotic properties of these statistics are examined.