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Journal articles on the topic 'Best linear unbiased estimator (BLUE)'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Wu, Jong-Wuu, Sheau-Chiann Chen, Wen-Chuan Lee, and Heng-Yi Lai. "Weighted Moments Estimators of the Parameters for the Extreme Value Distribution Based on the Multiply Type II Censored Sample." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/281624.

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We propose the weighted moments estimators (WMEs) of the location and scale parameters for the extreme value distribution based on the multiply type II censored sample. Simulated mean squared errors (MSEs) of best linear unbiased estimator (BLUE) and exact MSEs of WMEs are compared to study the behavior of different estimation methods. The results show the best estimator among the WMEs and BLUE under different combinations of censoring schemes.

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Hansen, Bruce E. "A Modern Gauss–Markov Theorem." Econometrica 90, no. 3 (2022): 1283–94. http://dx.doi.org/10.3982/ecta19255.

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This paper presents finite‐sample efficiency bounds for the core econometric problem of estimation of linear regression coefficients. We show that the classical Gauss–Markov theorem can be restated omitting the unnatural restriction to linear estimators, without adding any extra conditions. Our results are lower bounds on the variances of unbiased estimators. These lower bounds correspond to the variances of the the least squares estimator and the generalized least squares estimator, depending on the assumption on the error covariances. These results show that we can drop the label “linear est

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Isotalo, Jarkko, Märt Möls, and Simo Puntanen. "Invariance of the BLUE under the linear fixed and mixed effects models." Acta et Commentationes Universitatis Tartuensis de Mathematica 10 (December 31, 2006): 69–76. http://dx.doi.org/10.12697/acutm.2006.10.06.

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We consider the estimation of the parametric function X1β1 under the partitioned linear fixed effects model y=X1β1+X2β2+ε and the linear mixed effects model y=X1β1+X2γ2+ε, where γ2 is considered to be a random vector. Particularly, we consider when the best linear unbiased estimator, BLUE, of X1β1 under the linear fixed effects model equals the corresponding BLUE under the linear mixed effects model.

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IDOWU, Janet Iyabo, Olasunkanmi James OLADAPO, Abiola Timothy OWOLABİ, Kayode AYİNDE, and Oyinlade AKİNMOJU. "Combating Multicollinearity: A New Two-Parameter Approach." Nicel Bilimler Dergisi 5, no. 1 (2023): 1–31. http://dx.doi.org/10.51541/nicel.1084768.

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The ordinary least square (OLS) estimator is the Best Linear Unbiased Estimator (BLUE) when all linear regression model assumptions are valid. The OLS estimator, however, becomes inefficient in the presence of multicollinearity. To circumvent the problem of multicollinearity, various one and two-parameter estimators have been proposed. This paper a new two-parameter estimator called Liu-Kibria Lukman Estimator (LKL) estimator. The theoretical and simulation results show that the proposed estimator performs better than some existing estimators considered in this study under some conditions, usi

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Fransiska, Welly, Sigit Nugroho, and Ramya Rachmawati. "A Comparison of Weighted Least Square and Quantile Regression for Solving Heteroscedasticity in Simple Linear Regression." Journal of Statistics and Data Science 1, no. 1 (2022): 19–29. http://dx.doi.org/10.33369/jsds.v1i1.21011.

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Regression analysis is the study of the relationship between dependent variable and one or more independent variables. One of the important assumption that must be fulfilled to get the regression coefficient estimator Best Linear Unbiased Estimator (BLUE) is homoscedasticity. If the homoscedasticity assumption is violated then it is called heteroscedasticity. The consequences of heteroscedasticity are the estimator remain linear and unbiased, but it can cause estimator haven‘t a minimum variance so the estimator is no longer BLUE. The purpose of this study is to analyze and resolve the violati

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Shen, Bing-liang, Shuo Wang, Wang-xue Chen, and Meng Chen. "Fisher information for generalized Rayleigh distribution in ranked set sampling design with application to parameter estimation." Applied Mathematics-A Journal of Chinese Universities 37, no. 4 (2022): 615–30. http://dx.doi.org/10.1007/s11766-022-4450-5.

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AbstractIn the current paper, we considered the Fisher information matrix from the generalized Rayleigh distribution (GR) distribution in ranked set sampling (RSS). The numerical results show that the ranked set sample carries more information about λ and α than a simple random sample of equivalent size. In order to give more insight into the performance of RSS with respect to (w.r.t.) simple random sampling (SRS), a modified unbiased estimator and a modified best linear unbiased estimator (BLUE) of scale and shape λ and α from GR distribution in SRS and RSS are studied. The numerical results

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Hoque, Md Ariful, Zoran Bursac, and B. M. Golam Kibria. "Inferences About Two-Parameter Multicollinear Gaussian Linear Regression Models: An Empirical Type I Error and Power Comparison." Stats 8, no. 2 (2025): 28. https://doi.org/10.3390/stats8020028.

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In linear regression analysis, the independence assumption is crucial and the ordinary least square (OLS) estimator generally regarded as the Best Linear Unbiased Estimator (BLUE) is applied. However, multicollinearity can complicate the estimation of the effect of individual variables, leading to potential inaccurate statistical inferences. Because of this issue, different types of two-parameter estimators have been explored. This paper compares t-tests for assessing the significance of regression coefficients, including several two-parameter estimators. We conduct a Monte Carlo study to eval

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Babayan, Nikolay, and Mamikon S. Ginovyan. "On exponential decay of the variance of BLUE for the mean of a stationary sequence." Studia Scientiarum Mathematicarum Hungarica 56, no. 4 (2019): 482–91. http://dx.doi.org/10.1556/012.2019.56.4.1448.

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Abstract In this paper, we obtain necessary as well as sufficient conditions for exponential rate of decrease of the variance of the best linear unbiased estimator (BLUE) for the unknown mean of a stationary sequence possessing a spectral density. In particular, we show that a necessary condition for variance of BLUE to decrease to zero exponentially is that the spectral density vanishes on a set of positive Lebesgue measure in any vicinity of zero.

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Attwa, Rasha Abd El-Wahab, and Taha Radwan. "Different Methods for Estimating Default Parameters of Alpha Power-Transformed Power Distributions Using Record-Breaking Data." Symmetry 16, no. 1 (2023): 30. http://dx.doi.org/10.3390/sym16010030.

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The current study addresses the estimation of the default parameters of alpha power-transformed power (APTPO) distributions. For the location and scale parameters of the APTPO distributions, we provide coefficients for both the best linear unbiased estimators (BLUE) and the best linear invariant estimators (BLIE) methods. Furthermore, we establish a forecast for future records. The parameters of the APTPO distribution are estimated using the maximum likelihood estimation method (MLE). The goodness-of-fit test (using Akaike information criterion (AIC)) is computed using both the inter-record ti

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Yoon, Jin Hee, and Przemyslaw Grzegorzewski. "On Optimal and Asymptotic Properties of a Fuzzy L2 Estimator." Mathematics 8, no. 11 (2020): 1956. http://dx.doi.org/10.3390/math8111956.

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A fuzzy least squares estimator in the multiple with fuzzy-input–fuzzy-output linear regression model is considered. The paper provides a formula for the L2 estimator of the fuzzy regression model. This paper proposes several operations for fuzzy numbers and fuzzy matrices with fuzzy components and discussed some algebraic properties that are needed to use for proving theorems. Using the proposed operations, the formula for the variance, provided and this paper, proves that the estimators have several important optimal properties and asymptotic properties: they are Best Linear Unbiased Estimat

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Haslett, Stephen, Jarkko Isotalo, and Simo Puntanen. "Equalities between the BLUEs and BLUPs under the partitioned linear fixed model and the corresponding mixed model." Acta et Commentationes Universitatis Tartuensis de Mathematica 25, no. 2 (2021): 239–57. http://dx.doi.org/10.12697/acutm.2021.25.16.

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In this article we consider the partitioned fixed linear model F : y = X1β1 + X2β2 + ε" and the corresponding mixed model M : y =X1β1+X2u+ ε, where ε is a random error vector and u is a random effect vector. In 2006, Isotalo, M¨ols, and Puntanen found conditions under which an arbitrary representation of the best linear unbiased estimator (BLUE) of an estimable parametric function of β1 in the fixed model F remains BLUE in the mixed model M . In this paper we extend the results concerning further equalities arising from models F and M.

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KUMAR, N. K. SAJEEV, та A. R. SUMI. "USE OF RANKED SET SAMPLING IN THE ESTIMATION OF PARAMETERS 𝝁𝟐 AND 𝝈𝟐 OF MORGENSTERN TYPE BIVARIATE EXPONENTIAL DISTRIBUTION". IAPQR Transactions 47, № 1-2 (2023): 16–42. http://dx.doi.org/10.32381/iapqrt.2023.47.01-02.2.

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In this article we have derived the best linear unbiased estimator (BLUE) of the parameters 2 and 2 involved in the Morgenstern type bivariate exponential distribution by ranked set sampling (RSS). Also we have evaluated the BLUE's of 2 and 2 involved in the Morgenstern type bivariate exponential distribution by concomitants of ranked set sampling for some specific values of the sample size n. The efficiency comparison of the proposed estimators with that of the estimators using concomitants of order statistics and modified ranked set sampling is also made.

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Kumar, Vinay, and Tirthankar Ghosh. "Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region." Advances in Meteorology 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/5874270.

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This study uses downscaled rainfall datasets from 16 coupled climate models at high resolution of 25 km from 1987 to 2001. The multimodel superensemble scheme is widely tested for rainfall forecast over mid-latitude, subtropical, and, especially, various regions of the monsoonal belt. A well-known statistical estimation theoretic approach, namely, Best Linear Unbiased Estimator (BLUE), is examined on 16 member models. The results are compared with superensemble methodology based on various skill scores. Results show that BLUE is providing promising forecasts. As far as comparative studies are

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Virgantari, Fitria, Maya Widyastiti, and Natalia Ir Seno. "Comparison of Weights in Weighted Least Square Method For Handling Heteroscedasticity on Multiple Regression Model." International Journal of Mathematics, Statistics, and Computing 2, no. 2 (2024): 60–67. http://dx.doi.org/10.46336/ijmsc.v2i2.93.

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Regression analysis is the most popular and commonly used to determine causality between two or more variables. In regression analysis there are several assumptions that must be held, so that the property of the best linear unbiased estimator (BLUE) is still guaranteed. In fact, we often found violations of the assumptions. One of them was violations of the homoscedasticity or occurs heteroscedasticity. The impact of heteroscedasticity in the regression model is that the ordinary least square (OLS) estimator no longer has a minimum variance although still linear and unbiased. To handle this, w

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Haslett, Stephen. "Best linear unbiased estimation for varying probability with and without replacement sampling." Special Matrices 7, no. 1 (2019): 78–91. http://dx.doi.org/10.1515/spma-2019-0007.

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Abstract When sample survey data with complex design (stratification, clustering, unequal selection or inclusion probabilities, and weighting) are used for linear models, estimation of model parameters and their covariance matrices becomes complicated. Standard fitting techniques for sample surveys either model conditional on survey design variables, or use only design weights based on inclusion probabilities essentially assuming zero error covariance between all pairs of population elements. Design properties that link two units are not used. However, if population error structure is correlat

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VIANA, J. M. S., G. B. MUNDIM, R. O. DELIMA, F. F. E SILVA, and M. D. V. DE RESENDE. "Best linear unbiased prediction for genetic evaluation in reciprocal recurrent selection with popcorn populations." Journal of Agricultural Science 152, no. 3 (2013): 428–38. http://dx.doi.org/10.1017/s0021859613000270.

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SUMMARYThe objective of the present study was to present the theory and application of best linear unbiased prediction (BLUP) in reciprocal recurrent selection (RRS). Seven progeny tests from two RRS programmes with popcorn (Zea mays L. ssp. mays [syn. Zea mays L. ssp. everta (Sturtev.) Zhuk.]) populations were conducted and analysed for expansion volume and grain yield. The interpopulation half- and full-sib family models were fitted using ASReml software. Half-sib selection is equivalent to selection for the general combining ability (GCA) of the common parents. With inbred full-sib progeny

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Tian, Yongge. "An effective treatment of adding-up restrictions in the inference of a general linear model." Networks and Heterogeneous Media 8, no. 7 (2023): 15189–200. http://dx.doi.org/10.3934/math.2023775.

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<abstract><p>This article offers a general procedure of carrying out estimation and inference under a linear statistical model $ {\bf y} = {\bf X} \pmb{\beta} + \pmb{\varepsilon} $ with an adding-up restriction $ {\bf A} {\bf y} = {\bf b} $ to the observed random vector $ {\bf y} $. We first propose an available way of converting the adding-up restrictions to a linear matrix equation for $ \pmb{\beta} $ and a matrix equality for the covariance matrix of the error term $ \pmb{\varepsilon} $, which can help in combining the two model equations in certain consistent form. We then give

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Mokosolang, Christalia, Jantje Prang, and Mans Mananohas. "Analisis Heteroskedastisitas Pada Data Cross Section dengan White Heteroscedasticity Test dan Weighted Least Squares." d'CARTESIAN 4, no. 2 (2015): 172. http://dx.doi.org/10.35799/dc.4.2.2015.9056.

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Tujuan dari penelitian ini yaitu untuk menganalisis dan mengatasi pelanggaran asumsi heteroskedastisitas dengan white heteroscedasticity test dan weighted least squares.Dalam analisis regresi berganda, penting diselidiki adanya penyimpangan asumsi-asumsi, salah satunya asumsi homoskedastisitas. Apabila asumsi ini tak terpenuhi berarti terjadi heteroskedastisitas dan tidak lagi mempunyai sifat BLUE (Best Linear Unbiased Estimator). Dalam hal ini BLUE akan diperoleh dengan weighted least squares. Hasil menunjukkan bahwa white heteroscedasticity test dari data yang ditransformasi dengan weighted

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Andini, Agita, Etis Sunandi, Pepi Novianti, Idhia Sriliana, and Winalia Agwil. "Perbandingan Metode Regresi Ridge dan Jackknife Ridge Regression pada Data Tingkat Pengangguran Terbuka." Limits: Journal of Mathematics and Its Applications 22, no. 1 (2025): 77–84. https://doi.org/10.12962/limits.v22i1.3374.

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Regression analysis is a statistical technique used to analyze the relationship between predictor and response variables. One of the parameter estimation methods commonly used for regression analysis is Ordinary Least Squares. This method produces unbiased and efficient estimates, known as BLUE (Best Linear Unbiased Estimator). In multiple linear regression analysis involving more than one predictor variable, it is essential to meet model assumptions such as the absence of multicollinearity. Multicollinearity is a condition where predictor variables have a high correlation, which can disrupt t

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Sarmada, Sarmada, Ferra Yanuar, and Dodi Devianto. "Comparison of quantile regression and censored quantile regression methods in the case of chicken consumption." Desimal: Jurnal Matematika 6, no. 2 (2023): 245. http://dx.doi.org/10.24042/djm.v6i2.18949.

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The censored quantile regression method is a parameter estimation method that can be used to overcome censored data and BLUE (Best Linear Unbiased Estimator) assumptions that are not met. This research aims to compare the quantile regression method and the censored quantile regression method on data on chicken consumption cases in West Sumatra. The smallest RMSE (Root Mean Square Error) is an indicator of the goodness of the model. This research proves that the censored quantile regression method tends to produce smaller RMSE values than the quantile regression method. So it is concluded that

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FORKMAN, J., and H.-P. PIEPHO. "Performance of empirical BLUP and Bayesian prediction in small randomized complete block experiments." Journal of Agricultural Science 151, no. 3 (2012): 381–95. http://dx.doi.org/10.1017/s0021859612000445.

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SUMMARYThe model for analysis of randomized complete block (RCB) experiments usually includes two factors: block and treatment. If treatment is modelled as fixed, best linear unbiased estimation (BLUE) is used, and treatment means estimate expected means. If treatment is modelled as random, best linear unbiased prediction (BLUP) shrinks the treatment means towards the overall mean, which results in smaller root-mean-square error (RMSE) in prediction of means. This theoretical result holds provided the variance components are known, but in practice the variance components are estimated. BLUP us

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Rastegarnia, Amir, Mohammad Tinati, Behzad Mozaffari, and Azam Khalili. "A Localized Recursive Estimation Algorithm for Vector Parameter Estimation in AD HOC Wireless Sensor Networks." Journal of Electrical Engineering 61, no. 3 (2010): 171–76. http://dx.doi.org/10.2478/v10187-010-0024-5.

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A Localized Recursive Estimation Algorithm for Vector Parameter Estimation in AD HOC Wireless Sensor NetworksIn this paper a localized recursive estimation scheme for vector parameter estimation in ad hoc wireless sensor networks (WSNs) is proposed. In our setup, each sensor has a noisy measurement from a fixed, but unknown vector parameter. The classical best linear unbiased estimator (BLUE) cannot be implemented in a practical ad hoc sensor network due to its requirement to transmit all real-valued messages to a fusion center (FC). To address this problem, in this paper we propose a recursiv

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Farsipour, N. Sanjari, and A. Asgharzadeh. "Estimating the Common Mean of k Normal Populations with Known Variance." International Journal of Statistics and Probability 6, no. 4 (2017): 70. http://dx.doi.org/10.5539/ijsp.v6n470.

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Consider the problem of estimating the common mean of knormal populations with known variances. We study the admisibility of the Best linear Risk Unbiased Equivariant (BLRUE)estimator of the common mean of k normalpopulations underthe squared error and LINEX loss function when the variancesare known.

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Farsipour, N. Sanjari, and A. Asgharzadeh. "Estimating the Common Mean of k Normal Populations with Known Variance." International Journal of Statistics and Probability 6, no. 4 (2016): 70. http://dx.doi.org/10.5539/ijsp.v6n4p70.

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Consider the problem of estimating the common mean of knormal populations with known variances. We study the admisibility of the Best linear Risk Unbiased Equivariant (BLRUE)estimator of the common mean of k normalpopulations underthe squared error and LINEX loss function when the variancesare known.

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Wang, Mingshu, and Xiaolu Zhou. "GEOGRAPHY MATTERS IN ONLINE HOTEL REVIEWS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 573–76. http://dx.doi.org/10.5194/isprs-archives-xli-b2-573-2016.

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In resonance with the popularity of user-generated contents (UGC) and the volunteered geographic information (VGI), this study crowdsourced 77,098 hotel reviews of 220 hotels provided by U.S. reviewers in the city of San Francisco, 2002 to 2015. In this exploratory analysis, we have revealed that there is spatial dependence of customer satisfaction at different locations (of hotels), which violates the assumption that ordinary least-square (OLS) is the best linear unbiased estimator (BLUE); therefore, spatial model might be required for analysing any antecedents and consequences of such phenom

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Wang, Mingshu, and Xiaolu Zhou. "GEOGRAPHY MATTERS IN ONLINE HOTEL REVIEWS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 573–76. http://dx.doi.org/10.5194/isprsarchives-xli-b2-573-2016.

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In resonance with the popularity of user-generated contents (UGC) and the volunteered geographic information (VGI), this study crowdsourced 77,098 hotel reviews of 220 hotels provided by U.S. reviewers in the city of San Francisco, 2002 to 2015. In this exploratory analysis, we have revealed that there is spatial dependence of customer satisfaction at different locations (of hotels), which violates the assumption that ordinary least-square (OLS) is the best linear unbiased estimator (BLUE); therefore, spatial model might be required for analysing any antecedents and consequences of such phenom

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Nisius, Richard. "BLUE: Combining correlated estimates of physics observables within ROOT using the Best Linear Unbiased Estimate method." SoftwareX 11 (January 2020): 100468. http://dx.doi.org/10.1016/j.softx.2020.100468.

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Adejumo, Taiwo Joel, Kayode Ayinde, Ayobami Ibukun Okegbade, A. A. Akomolafe, Opeoluwa Abosede Oshuporu, and Sunday Olawale Koleoso. "Robust M Kibria Lukman estimator for linear regression model with outliers in the x-direction: simulations and applications." Science World Journal 19, no. 2 (2024): 440–54. http://dx.doi.org/10.4314/swj.v19i2.21.

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The Ordinary Least Square (OLS) estimator remains Best Linear Unbiased Estimator (BLUE) when all the assumptions surrounding it stay intact, but at an iota of violation of the assumptions, it becomes inefficient and unstable. Some causes of the violation are the multicollinearity and the presence of extreme values (outliers). Recently robust Kibria –Lukman based on M estimator was proposed by Majid et al. (2022) but when there are outlying cases in the y-direction. Since, outliers in the x-direction may be inevitable in the data set, therefore it becomes imperative to examine the performance o

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Young, Phil D., Joshua D. Patrick, and Dean M. Young. "Two Explicit Characterizations of the General Nonnegative-Definite Covariance Matrix Structure for Equality of BLUEs, WLSEs, and LSEs." International Journal of Statistics and Probability 9, no. 6 (2020): 108. http://dx.doi.org/10.5539/ijsp.v9n6p108.

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We provide a new, concise derivation of necessary and sufficient conditions for the explicit characterization of the general nonnegative-definite covariance structure V of a general Gauss-Markov model with E(y) and Var(y) such that the best linear unbiased estimator, the weighted least squares estimator, and the least squares estimator of X&beta; are identical. In addition, we derive a representation of the general nonnegative-definite covariance structure V defined above in terms of its Moore-Penrose pseudo-inverse.

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Beganu, Gabriela. "THE BEST LINEAR UNBIASED ESTIMATORS OF REGRESSION COEFFICIENTS IN A MULTIVARIATE GROWTH-CURVE MODEL. A COORDINATE-FREE APPROACH." Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales 31, no. 119 (2023): 267–73. http://dx.doi.org/10.18257/raccefyn.31(119).2007.2333.

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The problems of the existence of the best linear unbiased estimators (BLUE) and of their equality to the ordinary least squares estimators (OLSE) of the expected value of the observations are treated in a coordinate-free approach using a multivariate growth-curve model. It will be proved that the alternative forms of the necessary and sufficient conditions used in solving these problems are independent of the between-individuals design matrix of the model.

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Cheng, S. W., and C. H. Chou. "On the BLUE of the scale parameter of the generalized Pareto distribution." Tamkang Journal of Mathematics 31, no. 3 (2000): 165–74. http://dx.doi.org/10.5556/j.tkjm.31.2000.391.

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In this article, we will study the linear estimation of the scale parameter of the generalized Pareto distribution (GPD) which has the probability density function (p.d.f.)$$ f(x)=\left\{\begin{array}{ll} \sigma^{-1}(1-rx/\sigma)^{1/r-1},~~& r\not=0 \\ \sigma^{-1}\exp(-x/\sigma),&r=0. \end{array}\right.$$We first derive the expected value, variances and covariances of the order statistics from the GPD. Then proceed to find the best linear unbiased estimates of the scale parameter $\sigma$ based on a few order statistics selected from a complete sample or a type-II censored sample. Resu

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Sholeh, Maimun. "PENGARUH PENDIDIKAN, JUMLAH ANGGOTA KELUARGA, DAN AKSES INFORMASI TERHADAP KEMISKINAN DI IDONESIA." Jurnal Ekonomi dan Pendidikan 19, no. 1 (2022): 61–72. http://dx.doi.org/10.21831/jep.v19i1.55021.

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Penelitian ini bertujuan untuk mengetahui bagaimana pengaruh pendidikan, jumlah anggota keluarga dan akses informasi terhadap kemiskinan di Indonesia, dimana Salah satu tolok ukur kemiskinan dapat dilihat dari pendapatan. Data yang digunakan berupa data sekunder yang diperoleh dari Indonesia Family Life Survey 5 (IFLS5) tahun 2014. Teknik analisis menggunakan Ordinary Least Square (OLS) dengan asumsi Best, Linear, Unbiased, Estimator (BLUE). Data yang digunakan adalah data individu dan data rumah tangga dengan menyamakan household identity (hhid) dan personal identity (pid). Secara parsial pen

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Utika, Nadia, Maiyastri ., and Hazmira Yozza. "PERMASALAHAN AUTOKORELASI PADA ANALISIS REGRESI LINIER SEDERHANA." Jurnal Matematika UNAND 2, no. 2 (2013): 26. http://dx.doi.org/10.25077/jmu.2.2.26-34.2013.

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Pada analisis regresi linier sederhana model yang terbentuk harus memenuhibeberapa asumsi yang dikenal dengan asumsi linier klasik. Dalam asumsi linier klasikdinyatakan bahwa tidak terdapat korelasi antar galat yang disebut autokorelasi. Bila au-tokorelasi terjadi pada model regresi linier sederhana maka pengaruhnya terlihat padapenduga parameter yang tidak lagi BLUE (Best Linear Unbiased Estimator), yaitu pen-duga tidak bias dan linier, namun tidak lagi memiliki ragam yang minimum. Pengaruhautokorelasi juga dilihat menggunakan simulasi Monte Carlo. Simulasi Monte Carlomenggunakan nilai bias d

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Tri Subhi, Khusnudin, and Azka Al Azkiya. "Comparison of Cochrane-Orcutt and Hildreth-Lu Methods to Overcome Autocorrelation in Time Series Regression (Case Study of Gorontalo Province HDI 2010-2021)." Parameter: Journal of Statistics 2, no. 2 (2022): 30–36. http://dx.doi.org/10.22487/27765660.2022.v2.i2.15913.

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Time series data is data that is prone to autocorrelation. Autocorrelation is a violation of assumptions in Ordinary Least Square regression. The presence of autocorrelation can make parameter estimates, not BLUE (Best, Linear, Unbiased Estimator). Several methods to overcome autocorrelation include Cochrane-Orcutt and Hildreth-Lu methods. Therefore, this study aimed to compare the Cochrane-Orcutt and Hildreth-Lu methods to deal with autocorrelation in the time series regression of the Gorontalo Human Development Index case in 2010 2021. We used HDI data for Gorontalo Province from 2010-to 202

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UTHAMI, IDA AYU PRASETYA, I. KOMANG GDE SUKARSA, and I. PUTU EKA NILA KENCANA. "REGRESI KUANTIL MEDIAN UNTUK MENGATASI HETEROSKEDASTISITAS PADA ANALISIS REGRESI." E-Jurnal Matematika 2, no. 1 (2013): 6. http://dx.doi.org/10.24843/mtk.2013.v02.i01.p021.

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In regression analysis, the method used to estimate the parameters is Ordinary Least Squares (OLS). The principle of OLS is to minimize the sum of squares error. If any of the assumptions were not met, the results of the OLS estimates are no longer best, linear, and unbiased estimator (BLUE). One of the assumptions that must be met is the assumption about homoscedasticity, a condition in which the variance of the error is constant (same). Violation of the assumptions about homoscedasticity is referred to heteroscedasticity. When there exists heteroscedasticity, other regression techniques are

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KALLOUCHE, LAAMRI. "On The Calculation Of The Average Value Of The Strong Coupling Constant α_s, Using The Best Linear Unbiased Estimator (Blue) Method". Global Journal of Pure and Applied Sciences 28, № 2 (2022): 231–36. http://dx.doi.org/10.4314/gjpas.v28i2.13.

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Experiments to measure a single physical quantity often produce several estimates based on the same data, and which are hence correlated. We describe how to combine these correlated estimates in order to provide the best single answer, and also how to check whether the correlated estimates are mutually consistent. We discuss the properties of our technique, and illustrate its application by using it for a specific experiment which measured the strong coupling constant .In this work, we computed the mean value of the strong coupling constant by relying on the three measurement values of A

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Wardhani, Laksmi Prita, Alvida Mustika Rukmi, and Thaliah Fauz Ardamayanti. "Investigating the Association Between Carbon Dioxide (CO2) Emissions and Energy Consumption on the Economic Growth of Clusterized Countries in the World." European Journal of Pure and Applied Mathematics 16, no. 1 (2023): 657–69. http://dx.doi.org/10.29020/nybg.ejpam.v16i1.4614.

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This research aims to investigate the relationship between carbon dioxide (CO2) emissions and energy consumption on the economic growth of countries in the world based on Gross Domestic Product (GDP). We use a large volume of data related to CO2 emissions per capita and energy consumption per capita of each country in the world, so a clustering process is needed using the K-Means method, which divides the country into three cluster labels: high, medium, and low. Then, we performed multiple linear regression using the Generalized Least Square with Autoregressive (GLSAR) method for data that did

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Xu, Cheng-Dong, Jin-Feng Wang, Mao-Gui Hu, and Qing-Xiang Li. "Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE*." Journal of Climate 26, no. 19 (2013): 7452–63. http://dx.doi.org/10.1175/jcli-d-12-00633.1.

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Abstract Some climate datasets are incomplete at certain places and times. A novel technique called the point estimation model of Biased Sentinel Hospitals-based Area Disease Estimation (P-BSHADE) is introduced to interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calcul

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Surya, Dian, and Joko Subagio Santoso. "Analisis Pengaruh Harga Komoditas dan Pasar Modal Dunia Terhadap IHSG." Jurnal STEI Ekonomi 26, no. 2 (2017): 182–201. http://dx.doi.org/10.36406/jemi.v26i2.223.

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Tujuan dari penelitian ini yaitu untuk mengetahui pengaruh indikator ekonomi global dan beberapa indeks saham dunia terhadap IHSG. Metode analisis yang digunakan dalam penelitian ini adalah metode analisis regresi linear berganda. Salah satu syarat untuk melakukan uji analisis regresi linier berganda perlu dilakukan uji asumsi klasik. Hal ini diperlukan agar persamaan regresi yang dihasilkan bersifat BLUE (Best, Linear, Unbiased, Estimator). Selain itu untuk menilai goodness of fit suatu model dilakukan uji F, uji t, dan koefisien determinasi. Penelitian ini menggunakan data bulanan dari Janua

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Skøien, J. O., R. Merz, and G. Blöschl. "Top-kriging – geostatistics on stream networks." Hydrology and Earth System Sciences Discussions 2, no. 6 (2005): 2253–86. http://dx.doi.org/10.5194/hessd-2-2253-2005.

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Abstract. We present Top-kriging, or topological kriging, as a method for estimating streamflow-related variables in ungauged catchments. It takes both the area and the nested nature of catchments into account. The main appeal of the method is that it is a best linear unbiased estimator (BLUE) adapted for the case of stream networks without any additional assumptions. The concept builds on the work of Sauquet et al. (2000) and extends it in a number of ways. We test the method for the case of the specific 100-year flood for two Austrian regions. The method provides more plausible and, indeed,

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Skøien, J. O., R. Merz, and G. Blöschl. "Top-kriging - geostatistics on stream networks." Hydrology and Earth System Sciences 10, no. 2 (2006): 277–87. http://dx.doi.org/10.5194/hess-10-277-2006.

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Abstract. We present Top-kriging, or topological kriging, as a method for estimating streamflow-related variables in ungauged catchments. It takes both the area and the nested nature of catchments into account. The main appeal of the method is that it is a best linear unbiased estimator (BLUE) adapted for the case of stream networks without any additional assumptions. The concept is built on the work of Sauquet et al. (2000) and extends it in a number of ways. We test the method for the case of the specific 100-year flood for two Austrian regions. The method provides more plausible and, indeed

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Zahroh, Fatimatus, Ririn Irmadariyani, and Oktaviani Ari Wardhaningrum. "Komparasi Capital Asset Pricing Model dan Arbitrage Pricing Theory dalam Memprediksi Return Saham." Diversification: Journal of Economics and Management Studies 1, no. 1 (2024): 1–13. http://dx.doi.org/10.54373/djems.v1i1.968.

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This research aims to determine the comparison of the Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) in predicting stock returns of LQ45 index companies on the Indonesian Stock Exchange. Data analysis methods use Simple Regression Analysis, Multiple Regression Methods, and Difference tests. The results of data analysis show that there are differences between CAPM and APT in predicting stock returns. The emergence of varying differences in research results regarding the accuracy of the CAPM and APT models is caused by data disturbances that appear in the historical data u

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Lu, Peng-xin, Timothy L. White, and Dudley A. Huber. "Estimating Type B Genetic Correlations with Unbalanced Data and Heterogeneous Variances for Half-Sib Experiments." Forest Science 45, no. 4 (1999): 562–72. http://dx.doi.org/10.1093/forestscience/45.4.562.

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Abstract A statistical approach is proposed for estimating type B genetic correlations with unbalanced data and heterogeneous variances across environments. First, parental GCA (one-half of the parental breeding value) effects are predicted separately for each environment using best linear unbiased prediction (BLUP). Second, predicted parental GCAs are weighted by their prediction accuracies and Pearson correlation between weighted GCAs in two environments is obtained. Third, type B genetic correlation is calculated by dividing the Pearson correlation with the mean products of prediction accur

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Nugroho, Moch Erik, and Ignatia Martha Hendrati. "Pengaruh Pendapatan Asli Daerah (PAD), Dana Perimbangan, dan Pengeluaran Bidang kesehatan terhadap Indeks Pembangunan Manusia (IPM) Kabupaten Jombang Tahun 2007-2021." Jurnal Ilmiah Universitas Batanghari Jambi 24, no. 3 (2024): 2954. http://dx.doi.org/10.33087/jiubj.v24i3.5727.

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The aim of the research is to analyze the influence of a region's original income, balancing funds and health expenditure on the human development index (HDI) in Jombang Regency during the period 2007 - 2021. Using the multiple linear regression method by carrying out the Best Linear Unbiased Estimation (BLUE) test, and analysis hypothesis testing. The results of this research concluded that simultaneously the human development index (HDI) in Jombang Regency during the 2007-2021 period was influenced by local original income (PAD), balancing funds and expenditure in the health sector. However,

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Istriani, Indah. "Impact of reporting foreign exchange gains on capital market reactions (an empirical study of manufacturing companies listed on the Jakarta stock exchange in 2003-2004)." Digital Business Tren Bisnis Masa Depan 12, no. 2 (2021): 6–11. http://dx.doi.org/10.59651/digital.v12i2.65.

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The purpose of this study is to test empirically the impact of reporting foreign exchange gains on market reactions. The results of previous studies state that there is a correlation between the exchange rate (exchange rate) and stock prices. Based on these studies, researchers are motivated to conduct this research. This study uses Abnormal Return and Trading Volume Activity as a proxy for market reaction and uses 41 manufacturing companies listed on the Jakarta Stock Exchange in 2003-2004 as samples. The sample selection used purposive sampling method. Market reaction to the reporting of for

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Houllier, François, and Jean-Claude Pierrat. "Application des modèles statistiques spatio-temporels aux échantillonnages forestiers successifs." Canadian Journal of Forest Research 22, no. 12 (1992): 1988–95. http://dx.doi.org/10.1139/x92-259.

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When surveying the same forest on several successive occasions, sampling intensity may be reduced without any loss of precision by taking into account the spatial and temporal structures of the estimated variable. The theory of regionalized variables (RV) generalizes and improves the estimators derived from the classical sampling with partial replacement (SPR) theory. A general model accounting for both temporal and spatial structures is presented in the context of successive inventories. The best linear unbiased estimators (Blue) are derived by using the kriging technique. A comparison of RV

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Yi, Tianzhu, Zhihua He, Feng He, Zhen Dong, Manqing Wu, and Yongping Song. "A Compensation Method for Airborne SAR with Varying Accelerated Motion Error." Remote Sensing 10, no. 7 (2018): 1124. http://dx.doi.org/10.3390/rs10071124.

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Motion error is one of the most serious problems in airborne synthetic aperture radar (SAR) data processing. For a smoothly distributing backscatter scene or a seriously speed-varying velocity platform, the autofocusing performances of conventional algorithms, e.g., map-drift (MD) or phase gradient autofocus (PGA) are limited by their estimators. In this paper, combining the trajectories measured by global position system (GPS) and inertial navigation system (INS), we propose a novel error compensation method for varying accelerated airborne SAR based on the best linear unbiased estimation (BL

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Xiang, Bin, and Bailian Li. "A new mixed analytical method for genetic analysis of diallel data." Canadian Journal of Forest Research 31, no. 12 (2001): 2252–59. http://dx.doi.org/10.1139/x01-154.

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Diallel is a popular mating design used for crop and tree breeding programs, but its unique feature of a single observation with two levels of the same main effect, general combining ability (GCA), makes it difficult to analyze with standard statistical programs. A new approach using the SAS PROC MIXED is developed in this study for analyzing genetic data from diallel mating. Dummy variables for GCA effects were first constructed with SAS PROC IML, then PROC MIXED procedure was used to estimate variance components and to obtain BLUE (best linear unbiased estimators) of fixed effects and BLUP (

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Thirel, G., E. Martin, J. F. Mahfouf, S. Massart, S. Ricci, and F. Habets. "A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system." Hydrology and Earth System Sciences Discussions 7, no. 2 (2010): 2413–53. http://dx.doi.org/10.5194/hessd-7-2413-2010.

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Abstract. Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using observed streamflows in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-da

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Thirel, G., E. Martin, J. F. Mahfouf, S. Massart, S. Ricci, and F. Habets. "A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system." Hydrology and Earth System Sciences 14, no. 8 (2010): 1623–37. http://dx.doi.org/10.5194/hess-14-1623-2010.

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Abstract. Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using streamflow observations in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569

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