• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.525-534
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• 2003

If a restriction is imposed only to a (proper) subset of parameters of interest, we call it a local restriction. Statistical inference under a local restriction in multinomial setting is studied. The maximum likelihood estimation under a local restriction and likelihood ratio tests for and against a local restriction are discussed. A real data is analyzed for illustrative purpose.

• Journal of the Korean Statistical Society
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• v.27 no.3
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• pp.251-264
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• 1998

When the LRT is not feasible, we define quasi-LRT(QLRT) as a modification of the LRT Under some appropriate conditions the QLRT shares Bahadur optimality and Bartlett Adjustability with the LRT. When we can find maximum likelihood estimator under the null parameter space but not under the unrestricted parameter space, our QLRT is Bahadur optimal as is the LRT We suggest the stopping rule of the Newton-Raphson iterations for constructing the QLRT statistics which are Bartlett adjustable.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.967-976
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• 1999

In this paper we study the Hellinger distance based goodness-of-fit tests that are analogs of likelihood ratio tests. The minimum Hellinger distance estimator (MHDE) in normal models provides an excellent robust alternative to the usual maximum likelihood estimator. Our simulation results show that the Hellinger deviance test (Simpson 1989) based goodness-of-fit test is robust when data contain outliers. The proposed hellinger deviance test(Simpson 1989) is a more direcct method for obtaining robust inferences than an automated outlier screen method used before the likelihood ratio test data analysis.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.291-293
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• 2004

The aim of this study was to cross-validate a spatial probabilistic model of landslide likelihood ratios at Boun, Janghung and Yongin, in Korea, using a Geographic Information System (GIS). Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and field surveys. Maps of the topography, soil type, forest cover, lineaments and land cover were constructed from the spatial data sets. The 14 factors that influence landslide occurrence were extracted from the database and the likelihood ratio of each factor was computed. 'Landslide susceptibility maps were drawn for these three areas using likelihood ratios derived not only from the data for that area but also using the likelihood ratios calculated from each of the other two areas (nine maps in all) as a cross-check of the validity of the method For validation and cross-validation, the results of the analyses were compared, in each study area, with actual landslide locations. The validation and cross-validation of the results showed satisfactory agreement between the susceptibility map and the existing landslide locations.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.154-157
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• 1994

The evaluation of diagnostic tests attempts to obtain one or more statistical parameters which can indicate the intrinsic diagnostic utility of a test. Sensitivity. specificity and predictive value are not appropriate for this use. The likelihood ratio has been proposed as a useful measure when using a test to diagnose one of two disease states (e.g. disease present or absent). In this paper, we generalize the likelihood ratio concept to a situation in which the goal is to diagnose one of several non-overlapping disease states. A formula is derived to determine the post-test probability of a specific disease state. The post-test odds are shown to be related to the pre-test odds of a disease and to the usual likelihood ratios derived from considering the diagnosis between the target diagnosis and each alternate in turn. Hence, likelihood ratios derived from comparing pairs of diseases can be used to determine test utility in a multiple disease diagnostic situation.

• Bulletin of the Korean Mathematical Society
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• v.40 no.3
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• pp.373-383
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• 2003

We investigate a uniform local asymptotic normality for likelihood ratio processes based on an independent and identically distributed local asymptotic problem. Our tool is an empirical process theory.

• Journal of Family Resource Management and Policy Review
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• v.22 no.1
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• pp.63-78
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• 2018

This study examined the insolvency of debtors using multiple-indicator approaches and compared the outcomes across income levels with the 2016 'Household Financial and Welfare Survey'. This study used (1) the total debt to total assets ratio (DTA), (2) the total debt service ratio (DSR), and (3) the Household Default Risk Index (HDRI) recently developed by the Bank of Korea. Households in the lowest income quintile were more likely to be insolvent than any other income group. Demographics, such as age and gender of the household head, and most of the financial variables significantly increased the likelihood of insolvency based on the DTA. The number of household members and job status increased the likelihood of insolvency based on the DSR. Also, age, gender of the household head, and most of the financial variables increased the likelihood of household insolvency based on the HDRI after controlling for other demographics and financial variables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.4
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• pp.767-783
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• 2013

This paper aims at designing a selective demodulation scheme based on Log-likelihood Ratio threshold (SDLT) instead of the conventional adaptive demodulation (ADM) scheme, by using rateless codes. The major difference is that the Log-likelihood ratio (LLR) threshold is identified as a key factor to control the demodulation rate, while the ADM uses decision region set (DRS) to adjust the bit rate. In the 16-QAM SDLT scheme, we deduce the decision regions over an additive white Gaussian channel, corresponding to the variation of LLR threshold and channel states. We also derived the equations to calculate demodulation rate and bit error rate (BER), which could be proven by simulation results. We present an adaptation strategy for SDLT, and compare it with ADM and adaptive modulation (AM). The simulation results show that our scheme not only significantly outperforms the ADM in terms of BER, but also achieves a performance as good as the AM scheme. Moreover, the proposed scheme can support much more rate patterns over a wide range of channel states.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.63-63
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• 2003

The aim of this study is to apply and verify of Bayesian probability model, the likelihood ratio and statistical model, at Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite images, field surveys, and maps of the topography, soil type, forest cover, geology and land use were constructed to spatial database. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter and density of forest were extracted from the forest database. Land use was classified from the Landsat TM image satellite image. As each factor's ratings, the likelihood ratio coefficient were overlaid for landslide susceptibility mapping, Then the landslide susceptibility map was verified and compared using the existing landslide location. The results can be used to reduce hazards associated with landslides management and to plan land use and construction.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.3
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• pp.145-150
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• 2014

In this paper, we propose a novel scheme to improve the performance of a voice activity detection(VAD) which is based on the deep belief networks(DBN) with the likelihood ratio(LR). The proposed algorithm applies the DBN learning method which is trained in order to minimize the probability of detection error instead of the conventional decision rule using geometric mean. Experimental results show that the proposed algorithm yields better results compared to the conventional VAD algorithm in various noise environments.