• Molecules and Cells
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• 제45권7호
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• pp.444-453
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• 2022

Multivalent macromolecular interactions underlie dynamic regulation of diverse biological processes in ever-changing cellular states. These interactions often involve binding of multiple proteins to a linear lattice including intrinsically disordered proteins and the chromosomal DNA with many repeating recognition motifs. Quantitative understanding of such multivalent interactions on a linear lattice is crucial for exploring their unique regulatory potentials in the cellular processes. In this review, the distinctive molecular features of the linear lattice system are first discussed with a particular focus on the overlapping nature of potential protein binding sites within a lattice. Then, we introduce two general quantitative frameworks, combinatorial and conditional probability models, dealing with the overlap problem and relating the binding parameters to the experimentally measurable properties of the linear lattice-protein interactions. To this end, we present two specific examples where the quantitative models have been applied and further extended to provide biological insights into specific cellular processes. In the first case, the conditional probability model was extended to highlight the significant impact of nonspecific binding of transcription factors to the chromosomal DNA on gene-specific transcriptional activities. The second case presents the recently developed combinatorial models to unravel the complex organization of target protein binding sites within an intrinsically disordered region (IDR) of a nucleoporin. In particular, these models have suggested a unique function of IDRs as a molecular switch coupling distinct cellular processes. The quantitative models reviewed here are envisioned to further advance for dissection and functional studies of more complex systems including phase-separated biomolecular condensates.

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• 응용통계연구
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• 제15권1호
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• pp.73-84
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• 2002

계수에 대한 부등 제한조건이 있는 선형 회귀모형은 경제모형에서 가장 흔하게 다루어지는 것 중의 하나이다. 이는 특정 설명변수에 대한 계수의 부호를 음양 중 하나로 제한하거나 계수들에 대하여 순서적 관계를 주기 때문이다. 본 논문에서는 이러한 부등 제한이 있는 선형회귀 모형에서 유의한 설명변수의 선택을 해결하는 베이지안 기법을 고려한다. 베이지안 변수선택은 가능한 모든 모형의 사후확률 계산이 요구되는데 본 논문에서는 이러한 사후확률들을 동시에 계산하는 방법을 제시한다. 구체적으로 가장 일반적인 모형의 모수에 대한 사후표본을 깁스 표본기법을 적용시켜 얻은 후 이를 이용하여 모든 가능한 모형의 사후확률을 계산하고 실제적인 자료에 본 논문에서 제안된 방법을 적용시켜 본다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.209-216
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• 2003

Contingent valuation method (CVM) is a main evaluation method of nonmarket goods for which markets either do not exist at all or do exist only incompletely; an example is environmental good. A dichotomous choice approach, the most popular type of CVM in environmental economics, employs binary discrete choice models as statistical estimation models. In this paper, we propose a semiparametric dichotomous choice CVM method using local linear model of Fan and Gijbels (1996) in which probability distribution of error term is specified parametrically but latent structural function is specified nonparametrically. The computation procedures of the proposed method are illustrated with a simple design of simulations.

• 응용통계연구
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• 제28권2호
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• pp.241-250
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• 2015

경시적 자료 연구의 중요한 주제 중의 하나는 시간이 지남에 따라 개인의 건강 상태가 어떻게 변하는지를 추적하는 확률이다. 질병의 상태를 시간의 흐름에 따라 추적하는 것은 장기간에 걸친 임상적 관찰 연구의 계획과 분석, 그리고 질병의 예방과 치료에 중요한 의미를 지닌다. 본 논문에서는 두 다른 시점에서 각 개인의 건강 상태에 대한 조건부 확률을 추정해내는 순위 추적 확률에 대하여 연구하였다. 순위 추적 확률과 순위 추적 확률비를 추정하기 위하여 선형 혼합 효과 모형을 고려하였다. 본 논문의 방법은 아동을 대상으로 심혈관계 질환의 위험요인을 연구하는 역학 자료에 적용되었다.

• 응용통계연구
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• 제30권1호
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• pp.95-102
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• 2017

감마 일반화 선형모형은 포아송 분포 또는 이항 분포에 기반한 일반화 선형모형에 비해 적은 관심을 받아왔다. 따라서 감마 일반화 선형모형에서는 오래전에 개발된 통계적인 기법이 아직도 사용되고 있으며, 특히 산포 모수에 대해서는 근사 추정치가 여전히 사용되고 있다. 본 논문에서는 감마 일반화 선형 모형의 산포 모수에 대해 다양한 추정량들을 알아보고 수치 연구를 통해 그들의 효율성을 비교한다. 수치 실험의 결과 최대 가능도 추정량과 Cox-Reid의 수정된 최대 가능도 추정량이 기존의 근사 추정량에 비해 좋은 성능을 보임을 확인하였다.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.523-532
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• 1999

Generalized linear models have various applications for data arising from many kinds of statistical studies. Although the response variable is generally assumed to be generated from a wide class of probability distributions we focus on count data that are most often analyzed using binomial models for proportions or poisson models for rates. The methods and results presented here also apply to many other categorical data models in general due to the relationship between multinomial and poisson sampling. The novelty of the approach suggested here is that all conditional distribution s can be specified directly so that staraightforward Gibbs sampling is possible. The prior distribution consists of two stages. We rely on a normal nonconjugate prior at the first stage and a vague prior for hyperparameters at the second stage. The methods are demonstrated with an illustrative example using data collected by Rosenkranz and raftery(1994) concerning the number of hospital admissions due to back pain in Washington state.

• 인터넷정보학회논문지
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• 제19권6호
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• pp.101-112
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• 2018

A lot of manpower and budgets are being used to prevent fires, and only a small portion of the data generated during this process is used for disaster prevention activities. This study develops a prediction model of fire occurrence probability based on data mining in order to more actively use these data for disaster prevention activities. For this purpose, variables for predicting fire occurrence probability of various buildings were selected and data of construction administrative system, national fire information system, and Korea Fire Insurance Association were collected and integrated data set was constructed. After appropriate data cleansing and preprocessing, various data mining methodologies such as artificial neural network, decision trees, SVM, and Naive Bayesian were used to develop a prediction model of the fire occurrence probability of buildings. The most accurate model among the derived models is Linear SVM model which shows 68.42% as experimental data and 63.54% as verification data and it is the best model to predict fire occurrence probability of buildings. As this study develops the prediction model which uses only the set values of the specific ranges, future studies may explore more opportunites to use various setting values not shown in this study.

• 대한약침학회지
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• 제26권1호
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• pp.67-76
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• 2023

Objectives: Medical services are closely related to individual health and welfare, and health status in childhood or adolescence is widely recognized to be related to many socioeconomic outcomes. Therefore, providing appropriate medical services in childhood and adolescence is important. We aimed to investigate the determinants of traditional Korean medical services (TKMS) usage by children aged < 19 years. The focus was on the role of their parents' experiences with TKMS in determining TKMS use by children. Methods: Using a representative sample in South Korea, we conducted a regression analysis to assess how parents' experience with TKMS affects the probability of their children using TKMS. Results: We found parents' experience with TKMS to have a significantly positive effect on the probability of TKMS use by children and parents' biological information, such as age and sex, to affect the probability of TKMS use. Specifically, parents' experiences with TKMS generally increased the probability of children using TKMS by approximately 20%. Conclusion: This study's results suggest that considering parents' opinions and providing them the opportunity to participate in programs that enhance young children's use of TKMS may be effective.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.587-594
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• 2009

이 논문에서는 선형회귀모형의 오차항에 대한 변화점 검정 문제를 다룬다. 고정 혹은 변동 모형의 독립 변수와 약한 종속성을 가지는 오차항을 가정하는 관계로 통상적인 중회귀모형뿐만 아니라 ARMA 등의 시계열 모형까지 본 논문에서 포괄한다고 하겠다. 오차항의 분포 변화를 검정하기 위하여 회귀모형의 잔차에 기초한 확률밀도함수 추정값을 이용한다. 적절한 가정하에서 잔차를 이용한 검정이 실제 오차를 이용한 경우와 동일한 극한 분포를 가짐을 보였다.