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Correspondence analysis for studying association between geography and cancer
송준진,Pingjian Yu,Yuan Ren,Ming-Hua Chung 한국데이터정보과학회 2009 한국데이터정보과학회지 Vol.20 No.5
Geographical location carries information such as demography, local economy, environment, and life styles, which could be the sources of cancer occurrence. Analyzing geographical location associated with cancer occurrence can be instructive to physicians, patients, and health administrators regarding resource allocation, expenditures, prophylaxis and treatments. In this paper, we explored the correspondence relationship between geographical locations and mortality rates of the cancers using correspondence analysis and illustrated the approach with the mortality rates of the top 10 cancers in the 75 counties in Arkansas from 2001 to 2005. Geographical variations with respect to the mortality rates of cancers are evaluated across Arkansas counties. Based on the contingency table, correspondence analysis model is developed and the simple indices which indicate the degree to which the regions and the cancers affect each other are calculated. Quantitative results are visualized and mapped in two-dimensional graphs.
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Spatial Interpolation of Gauge Measured Rainfall Using Compressed Sensing
류수록,송준진,김용구,정성화,도영해,이규원 한국기상학회 2021 Asia-Pacific Journal of Atmospheric Sciences Vol.57 No.2
In this work, we suggest new spatial precipitation interpolation schemes using compressed sensing (CS), which is a new framework for signal acquisition and smart sensor design. Using CS, the precipitation maps are recovered in high resolution by obtaining sparse coefficients of radial basis functions(RBFs). Two types of methods are designed according to the construction methods of CS matrix. In the first type, the CS matrix is derived as the product of an m × n (n m) weights matrix of inverse distance weighting (IDW) and an n × n radial basis function (RBF) matrix. The second type of CS matrix consists of an m × n RBF matrix that depends on a few observation vectors and a number of n unknown vectors. The advantage of the proposed CS methods is that it can be represented at a high resolution because it is interpolated based on a large number of bases (or degrees of freedom). This prevents the variance value from being much smaller than the actual value due to interpolation using a few observation scales. To test our CS interpolation schemes, interpolation results were compared with IDW, Ordinary Kriging (OK) and RBF interpolation methods for analytic test function and some actual rainfall data. In the case of an analytic test function, when the proposed method is compared at high resolution, the error from the true value is the smallest. In real rainfall data, comparison with real values is not possible at high resolutions, but the error with the observed data is the smallest in terms of ‘spatial variogram’. In addition, the proposed CS method generates hight resolution data from rainfall cases, showing promising results when identifying peaks. In this work, we suggest new spatial precipitation interpolation schemes using compressed sensing (CS), which is a new framework for signal acquisition and smart sensor design. Using CS, the precipitation maps are recovered in high resolution by obtaining sparse coefficients of radial basis functions(RBFs). Two types of methods are designed according to the construction methods of CS matrix. In the first type, the CS matrix is derived as the product of an m × n ( n ≫ m ) weights matrix of inverse distance weighting (IDW) and an n × n radial basis function (RBF) matrix. The second type of CS matrix consists of an m × n RBF matrix that depends on a few observation vectors and a number of n unknown vectors. The advantage of the proposed CS methods is that it can be represented at a high resolution because it is interpolated based on a large number of bases (or degrees of freedom). This prevents the variance value from being much smaller than the actual value due to interpolation using a few observation scales. To test our CS interpolation schemes, interpolation results were compared with IDW, Ordinary Kriging (OK) and RBF interpolation methods for analytic test function and some actual rainfall data. In the case of an analytic test function, when the proposed method is compared at high resolution, the error from the true value is the smallest. In real rainfall data, comparison with real values is not possible at high resolutions, but the error with the observed data is the smallest in terms of ‘spatial variogram’. In addition, the proposed CS method generates hight resolution data from rainfall cases, showing promising results when identifying peaks.
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Bayesian estimation for Rayleigh models
오지은,송준진,손중권 한국데이터정보과학회 2017 한국데이터정보과학회지 Vol.28 No.4
The Rayleigh distribution has been commonly used in life time testing studies of the probability of surviving until mission time. We focus on a reliability function of the Rayleigh distribution and deal with prior distribution on R(t). This paper is an effort to obtain Bayes estimators of rayleigh distribution with three different prior distribution on the reliability function; a noninformative prior, uniform prior and inverse gamma prior. We have found the Bayes estimator and predictive density function of a future observation y with each prior distribution. We compare the performance of the Bayes estimators under different sample size and in simulation study. We also derive the most plausible region, prediction intervals for a future observation.
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Spatio-temporal modeling to reduce women's fear of crime
전영은,강석복,서정인,송준진 한국데이터정보과학회 2022 한국데이터정보과학회지 Vol.33 No.2
As rape and forced indecent act crimes are increasing in Korea, fears of this are also growing. Urban experts have long recognized crime and its fear as a major challenge for sustainable cities because such things degrade the quality of life by threatening the safety of citizens. So, this research analyzes rape and forced indecent act data occurred in Seoul from 2015 to 2018 using the spatio-temporal model. For the spatio-temporal model, three types of models are considered: classical parametric, dynamic nonparametric trend, and space-time interaction nonparametric trend models. To find out how factors considered affect rape and forced indecent act crimes, the integrated nested Laplace approximation (INLA) technique based on R software is applied. This approach proposes efficient strategies to sustain women's safe everyday living, analyzing important risk factors affecting rape and forced indecent act crimes and the relative risk of each region.
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The effect of health care reform: Testing the stability of systematic risk
Daniel K. Sewell,송준진 한국데이터정보과학회 2010 한국데이터정보과학회지 Vol.21 No.5
As the U.S. Congress has continued to debate over the health care reform pushed by President Obama, there is an ample reason to believe that the systematic risk of the health care industry, especially health care plan providers, is increasing. This study measures and compares the systematic risk of two health care industry indexes and one portfolio of health care plan providers from before and after the introduction of the health care legislation into Congress in September, 2009. The Capital Asset Pricing Model (CAPM) is used to measure the systematic risk, and a dummy variable approach and the Chow test are used to formally compare the systematic risk from before and after the introduction of the legislation.
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