• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.365-372
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• 2019

We propose a method to estimate unknown parameters(e.g., target amplitude and clutter parameters) in the generalized likelihood ratio test(GLRT) using maximum likelihood estimation and the Newton-Raphson method. When detecting targets in a clutter environ- ment, it is important to establish a modular model of clutter similar to the actual environment. These correlated clutter models can be generated using spherically invariant random vectors. We obtain the GLRT of the generated clutter model and check its detection probability using estimated parameters.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.129-139
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• 2004

We consider the problem of testing for outliers in generalized linear model. We proceed by first specifying a mean shift outlier model, assuming the suspect set of ourliers is known. Given this model, we discuss standard approaches to obtaining score test for outliers as an alternative to the likelihood ratio test.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.475-484
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• 2018

Gamma generalized linear models are useful for non-negative and skewed responses. However, these models have received less attention than Poisson and binomial generalized linear models. In particular, hypothesis testing for the significance of regression coefficients has not been thoroughly studied. In this paper we assess the performance of various test statistics for gamma generalized linear models based on numerical studies. Our results show that the likelihood ratio test and F-type test are generally recommended and that the partial deviance test should be avoided in practice.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.225-237
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• 1994

A generalized likelihood ratio test is developed to detect an outlier associated with monitoring nuclear proliferation. While the classical outlier detection methods consider continuous variables only, our approach allows both continuous and discrete variables or a mixture of continuous and discrete variables to be used. In addition, our method is free of the normality assumption, which is the key assumption in most of the classical methods. The proposed test is constructed by applying the bootstrap to a generalized likelihood ratio. We investigate the performance of the test by studying the power with simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2763-2782
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• 2014

In this paper, a generalized likelihood ratio test (GLRT) is proposed for cyclostationary multi-antenna spectrum sensing in cognitive radio systems, which takes into account the cyclic autocorrelations obtained from all the receiver antennas and the cyclic cross-correlations obtained from all pairs of receiver antennas. The proposed GLRT employs a different hypotheses problem formulation and a different asymptotic covariance estimation method, which are proved to be more suitable for multi-antenna systems than those employed by the $Dandawat{\acute{e}}$-Giannakis algorithm. Moreover, we derive the asymptotic distributions of the proposed test statistics, and prove the constant false alarm rate property of the proposed algorithm. Extensive simulations are conducted, showing that the proposed GLRT can achieve better detection performance than the $Dandawat{\acute{e}}$-Giannakis algorithm and its extension for multi-antenna cases.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.31-52
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• 2008

In this paper generalized version of the geometric distribution is introduced. This distribution can be considered as a two-parameter generalization of the discrete geometric distribution. The main statistical and reliability properties of this distribution are discussed. Two methods of estimation, namely maximum likelihood method and the method of moments are used to estimate the parameters of this distribution. Simulation is utilized to calculate these estimates and to study some of their properties. Also, asymptotic confidence limits are established for the maximum likelihood estimates. Finally, the appropriateness of this new distribution for a set of real data, compared with the geometric distribution, is shown by using the likelihood ratio test and the Kolmogorove-Smirnove test.

• Communications for Statistical Applications and Methods
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• v.20 no.5
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• pp.417-425
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• 2013

We consider an extended version of a logarithmic series distribution and discuss the estimation of its parameters by the method of moments and the method of maximum likelihood. Test procedures are suggested to test the significance of the additional parameter of this distribution and all procedures are illustrated with the help of real life data sets. In addition, a simulation study is conducted to assess the performance of the estimators.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.55-55
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• 2000

This paper presents a fault detection and isolation(FDI) method based on Ceneralized Likelihood Ratio(GLR) test for the tightly coupled INS/GPS. State and measurement GLR tests detect INS or GPS fault. Once the fault is detected, Multi-hypothesized GLR scheme performs the fault isolation between INS and GPS and find which satellite malfunctions. Simulation results show that the GLR method is effective enough to detect and isolate a fault of the integrated navigation system.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.585-594
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• 2010

This study, investigates the effects of dispersion parameters between two response variables in zero-truncated bivariate generalized Poisson distributions. A Monte Carlo study shows that the zero-truncated bivariate Poisson and negative binomial models fit poorly wherein the zero-truncated bivariate count data has heterogeneous dispersion parameters on dependent variables. In addition, we derive the score test for testing the equality of the dispersion parameters and compare its efficiency with the likelihood ratio test.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.647-663
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• 2017

Weibull distribution is a popular distribution for modeling lifetimes because it reflects the characteristics of failure adequately and it models either increasing or decreasing failure rates simply. It is a standard method of the lifetimes test to wait until all samples failed; however, censoring can occur due to some realistic limitations. In this paper, we propose a generalized likelihood ratio (GLR) chart to monitor changes in the scale parameter for type I right-censored Weibull lifetime data. We also compare the performance of the proposed GLR chart with two CUSUM charts proposed earlier using average run length (ARL). Simulation results show that the Weibull GLR chart is effective to detect a wide range of shift sizes when the shape parameter and sample size are large and the censoring rate is not too high.