• 제목/요약/키워드: Simple Linear Regression

검색결과 423건 처리시간 0.022초

통계적 방법에 근거한 AMSU-A 복사자료의 전처리 및 편향보정 (Pre-processing and Bias Correction for AMSU-A Radiance Data Based on Statistical Methods)

  • 이시혜;김상일;전형욱;김주혜;강전호
    • 대기
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    • 제24권4호
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    • pp.491-502
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    • 2014
  • As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.

Comparison of Confidence Intervals on Variance Component In a Simple Linear Regression Model with Unbalanced Nested Error Structure

  • Park, Dong Joon;Park, Sun-Young;Han, Man-Ho
    • Communications for Statistical Applications and Methods
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    • 제9권2호
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    • pp.459-471
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    • 2002
  • In applications using a linear regression model with nested error structure, one might be interested in making inferences concerning variance components. This article proposes approximate confidence intervals on the variance component of the primary level in a simple linear regression model with an unbalanced nested error structure. The intervals are compared using computer simulation and recommendations are provided for selecting an appropriate interval.

Hidden Truncation Normal Regression

  • Kim, Sungsu
    • Communications for Statistical Applications and Methods
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    • 제19권6호
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    • pp.793-798
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    • 2012
  • In this paper, we propose regression methods based on the likelihood function. We assume Arnold-Beaver Skew Normal(ABSN) errors in a simple linear regression model. It was shown that the novel method performs better with an asymmetric data set compared to the usual regression model with the Gaussian errors. The utility of a novel method is demonstrated through simulation and real data sets.

Statistical notes for clinical researchers: simple linear regression 3 - residual analysis

  • Kim, Hae-Young
    • Restorative Dentistry and Endodontics
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    • 제44권1호
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    • pp.11.1-11.8
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    • 2019
  • In the previous sections, simple linear regression (SLR) 1 and 2, we developed a SLR model and evaluated its predictability. To obtain the best fitted line the intercept and slope were calculated by using the least square method. Predictability of the model was assessed by the proportion of the explained variability among the total variation of the response variable. In this session, we will discuss four basic assumptions of regression models for justification of the estimated regression model and residual analysis to check them.

Statistical notes for clinical researchers: simple linear regression 2 - evaluation of regression line

  • Kim, Hae-Young
    • Restorative Dentistry and Endodontics
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    • 제43권3호
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    • pp.34.1-34.5
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    • 2018
  • In the previous section, we established a simple linear regression line by finding the slope and intercept using the least square method as: ${\hat{Y}}=30.79+0.71X$. Finding the regression line was a mathematical procedure. After that we need to evaluate the usefulness or effectiveness of the regression line, whether the regression model helps explain the variability of the dependent variable. Also, statistical inference of the regression line is required to make a conclusion at the population level, because practically, we work with a sample, which is a small part of population. Basic assumption of sampling method is simple random sampling.

Estimation of slope , βusing the Sequential Slope in Simple Linear Regression Model

  • Choi, Yong;Kim, Dongjae
    • Communications for Statistical Applications and Methods
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    • 제10권2호
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    • pp.257-266
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    • 2003
  • Distribution-free estimation methods are proposed for slope, $\beta$ in the simple linear regression model. In this paper, we suggest the point estimators using the sequential slope based on sign test and Wilcoxon signed rank test. Also confidence intervals are presented for each estimation methods. Monte Carlo simulation study is carried out to compare the efficiency of these methods with least square method and Theil´s method. Some properties for the proposed methods are discussed.

Interval Estimation for Sum of Variance Components in a Simple Linear Regression Model with Unbalanced Nested Error Structure

  • Park, Dong-Joon
    • Communications for Statistical Applications and Methods
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    • 제10권2호
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    • pp.361-370
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    • 2003
  • Those who are interested in making inferences concerning linear combination of valiance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods. The methods are applied to a numerical example and recommendations are given for choosing a proper interval.

Confidence Interval For Sum Of Variance Components In A Simple Linear Regression Model With Unbalanced Nested Error Structure

  • Park, Dong-Joon
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2003년도 춘계 학술발표회 논문집
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    • pp.75-78
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    • 2003
  • Those who are interested in making inferences concerning linear combination of variance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods.

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단순 선형회귀 모형에서 자기공분산에 근거한 최적 추정 방법 (An estimation method based on autocovariance in the simple linear regression model)

  • 박철용
    • Journal of the Korean Data and Information Science Society
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    • 제20권2호
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    • pp.251-260
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    • 2009
  • 이 논문에서는 단순 선형회귀 모형에서 회귀 계수의 최적 추정량을 구할 수 있는 자기공분산에 근거한 추정 방법을 제시하였다. 이 방법이 직관적으로 매혹적이지는 않지만 이 최적 추정량이 해당 회귀 계수의 불편추정량이 된다. 설명변수가 0과 1사이의 균등간격의 값을 가지면, 오차가 자기회귀 이동평균 모형을 따르면 성립하는 조건 하에서 이 최적 추정량이 최소제곱 추정량과 점근적으로 통일한 분포를 가진다는 것을 보였다. 추가적으로 똑같은 조건 하에서 이 최적 추정량이 해당 회귀 계수에 확률상 수렴한다는 것을 자체적으로 입증하였다.

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