• Title, Summary, Keyword: error variance

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Linear Measurement Error Variance Estimation based on the Complex Sample Survey Data

  • Heo, Sunyeong;Chang, Duk-Joon
    • Journal of the Chosun Natural Science
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    • v.5 no.3
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    • pp.157-162
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    • 2012
  • Measurement error is one of main source of error in survey. It is generally defined as the difference between an observed value and an underlying true value. An observed value with error may be expressed as a function of the true value plus error term. In some cases, the measurement error variance may be also a function of the unknown true value. The error variance function can be rewritten as a function of true value multiplied by a scale factor. This research explore methods for estimation of the measurement error variance based on the data from complex sampling design. We consider the case in which the variance of mesurement error is a linear function of unknown true value, and the error variance scale factor is small. We applied our results to the U.S. Third National Health and Nutrition Examination Survey (the U.S. NHANES III) data for empirical analyses, which has replicate measurements for relatively small subset of initial respondents's group.

Hierarchical Bayes Estimators of the Error Variance in Two-Way ANOVA Models

  • Chang, In Hong;Kim, Byung Hwee
    • Communications for Statistical Applications and Methods
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    • v.9 no.2
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    • pp.315-324
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    • 2002
  • For estimating the error variance under the relative squared error loss in two-way analysis of variance models, we provide a class of hierarchical Bayes estimators and then derive a subclass of the hierarchical Bayes estimators, each member of which dominates the best multiple of the error sum of squares which is known to be minimax. We also identify a subclass of non-minimax hierarchical Bayes estimators.

Constant Error Variance Assumption in Random Effects Linear Model

  • Ahn, Chul-Hwan
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.296-302
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    • 1995
  • When heteroscedasticity occurs in random effects linear model, the error variance may depend on the values of one or more of the explanatory variables or on other relevant quantities such as time or spatial ordering. In this paper we derive a score test as a diagnostic tool for detecting non-constant error variance in random effefts linear model based on the model expansion on error variance. This score test is compared to loglikelihood ratio test.

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The Economic Design of $\bar{x}$ -S Chart Considering Measurement Error (측정오차를 고려한 $\bar{x}$ -S 관리도의 경제적 설계)

  • 유영창;강창욱
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.23 no.61
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    • pp.89-98
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    • 2000
  • For statistical process control, the process data are collected by the measurement system. But, the measurement system may have instrument error or/and operator error. In the measured values of products, the total observed variance consists of process variance and variance due to error of measurement system. In this paper, we design more practical T-s control chart considering estimated measurement error The effects of measurement error on the expected total cost and design parameters are investigated.

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Hierarchical Bayes Estimators of the Error Variance in Balanced Fixed-Effects Two-Way ANOVA Models

  • Kim, Byung-Hwee;Dong, Kyung-Hwa
    • Communications for Statistical Applications and Methods
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    • v.6 no.2
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    • pp.487-500
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    • 1999
  • We propose a class of hierarchical Bayes estimators of the error variance under the relative squared error loss in balanced fixed-effects two-way analysis of variance models. Also we provide analytic expressions for the risk improvement of the hierarchical Bayes estimators over multiples of the error sum of squares. Using these expressions we identify a subclass of the hierarchical Bayes estimators each member of which dominates the best multiple of the error sum of squares which is known to be minimax. Numerical values of the percentage risk improvement are given in some special cases.

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On statistical properties of some dierence-based error variance estimators in nonparametric regression with a finite sample

  • Park, Chun-Gun
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.3
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    • pp.575-587
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    • 2011
  • We investigate some statistical properties of several dierence-based error variance estimators in nonparametric regression model. Most of existing dierence-based methods are developed under asymptotical properties. Our focus is on the exact form of mean and variance for the lag-k dierence-based estimator and the second-order dierence-based estimator in a nite sample size. Our approach can be extended to Tong's estimator (2005) and be helpful to obtain optimal k.

ON THE ADMISSIBILITY OF HIERARCHICAL BAYES ESTIMATORS

  • Kim Byung-Hwee;Chang In-Hong
    • Journal of the Korean Statistical Society
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    • v.35 no.3
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    • pp.317-329
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    • 2006
  • In the problem of estimating the error variance in the balanced fixed- effects one-way analysis of variance (ANOVA) model, Ghosh (1994) proposed hierarchical Bayes estimators and raised a conjecture for which all of his hierarchical Bayes estimators are admissible. In this paper we prove this conjecture is true by representing one-way ANOVA model to the distributional form of a multiparameter exponential family.

Bayesian Analysis for the Error Variance in a Two-Way Mixed-Effects ANOVA Model Using Noninformative Priors (무정보 사전분포를 이용한 이원배치 혼합효과 분산분석모형에서 오차분산에 대한 베이지안 분석)

  • 장인홍;김병휘
    • The Korean Journal of Applied Statistics
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    • v.15 no.2
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    • pp.405-414
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    • 2002
  • We consider the problem of estimating the error variance of in a two-way mixed-effects ANOVA model using noninformative priors. First, we derive Jeffreys' prior, a reference prior, and matching priors. We then provide marginal posterior distributions under those noninformative priors. Finally, we provide graphs of marginal posterior densities of the error variance and credible intervals for the error variance in two real data set and compare these credible intervals.

The Distributions of Variance Components in Two Stage Regression Model

  • Park, Dong-Joon
    • Journal of the Korean Data and Information Science Society
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    • v.7 no.1
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    • pp.87-92
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    • 1996
  • A regression model with nested erroe structure is considered. The regression model includes two error terms that are independent and normally distributed with zero means and constant variances. This error structure of the model gives correlated response variables. The distributions of variance components in the regression model with nested error structure are dervied by using theorems for quadratic forms.

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Interrelationship of phase-error variance and correlation coefficient in microwave imaging (마이크로웨이브 이미징에서 위상오차 분산과 코릴레이션 계수와의 상호관계)

  • 강봉순;장훈기
    • Journal of the Korean Institute of Telematics and Electronics D
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    • v.34D no.10
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    • pp.1-6
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    • 1997
  • This paper presents the theoretical derivaion relating and image correlationcoeffcient capable of assessing image quality, with phase-error variance in antenna aperture domain. We show that when the phase-error variance of a range bin selected as an adaptive beamformer is known, the quality of the reconstructed image is predictable and moreover, the resultant correlation coeffcient is obviously greater than the derive dlower boudn. To support the derivation, real data are used for image formation where the dominant scatterer algorithm (DSA) is applied for phase compensations.

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