Kriging made easy or assessing the linear unbiased minimum variance estimate by adapting the projection theorem

1984 ◽  
Vol 19 (1) ◽  
Author(s):  
Matti Heikkil� ◽  
Esko Koistinen
Keyword(s):  
Minimum Variance ◽  
Projection Theorem ◽  
Variance Estimate

scholarly journals AN APPROXIMATION OF THE MINIMUM-VARIANCE ESTIMATOR OF HERITABILITY BASED ON VARIANCE COMPONENT ANALYSIS

Genetics ◽  
1981 ◽  
Vol 98 (2) ◽  
pp. 417-426
Author(s):  
M Grossman ◽  
H W Norton
Keyword(s):  
Minimum Variance ◽  
Variance Component Analysis ◽  
Weighted Estimate ◽  
Sampling Variance ◽  
Variance Estimator ◽  
Hierarchical Analysis ◽  
Components Of Variance ◽  
Minimum Variance Estimator ◽  
Variance Estimate ◽  
Desirable Behavior

ABSTRACT An approximate minimum-variance estimate of heritability (h  2) is proposed, using the sire and dam components of variance from a hierarchical analysis of variance. The minimum sampling variance is derived for unbalanced data. Optimum structures for the estimation of h  2 are given for the balanced case. The degree to which ĥ  2 is more precise than the equally weighted estimate ĥ  2  S+D is a function of the size and structure of the sample used. However, computer simulation reveals that ĥ  2 has less desirable behavior than ĥ  2  S+D. An iterative procedure improved the estimation of h  2, especially in small populations, when those values of ĥ  2  S or ĥ  2  D outside the range of the parameter were constrained to zero or unity.

Properties of the beta coefficient of the global minimum variance portfolio

2020 ◽  
Vol 8 (1) ◽  
pp. 11-21
Author(s):  
S. M. Yaroshko ◽  
◽  
M. V. Zabolotskyy ◽  
T. M. Zabolotskyy ◽  
◽  
...  
Keyword(s):  
Confidence Interval ◽  
Asymptotic Distribution ◽  
Global Minimum ◽  
Asset Returns ◽  
Minimum Variance ◽  
Benchmark Portfolio ◽  
Beta Coefficient ◽  
Minimum Variance Portfolio ◽  
Global Minimum Variance Portfolio ◽  
Daily Returns

The paper is devoted to the investigation of statistical properties of the sample estimator of the beta coefficient in the case when the weights of benchmark portfolio are constant and for the target portfolio, the global minimum variance portfolio is taken. We provide the asymptotic distribution of the sample estimator of the beta coefficient assuming that the asset returns are multivariate normally distributed. Based on the asymptotic distribution we construct the confidence interval for the beta coefficient. We use the daily returns on the assets included in the DAX index for the period from 01.01.2018 to 30.09.2019 to compare empirical and asymptotic means, variances and densities of the standardized estimator for the beta coefficient. We obtain that the bias of the sample estimator converges to zero very slowly for a large number of assets in the portfolio. We present the adjusted estimator of the beta coefficient for which convergence of the empirical variances to the asymptotic ones is not significantly slower than for a sample estimator but the bias of the adjusted estimator is significantly smaller.

Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties

2014 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Set Foong Ng ◽  
Pei Eng Ch’ng ◽  
Yee Ming Chew ◽  
Kok Shien Ng
Keyword(s):  
Soil Properties ◽  
Lagrange Multipliers ◽  
Unbiased Estimator ◽  
Soil Property ◽  
Minimum Variance ◽  
Distance Method ◽  
True Value ◽  
A Site ◽  
Method Of Lagrange Multipliers ◽  
Inverse Distance

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

ANALISIS PORTOFOLIO INVESTASI PADA SAHAM LQ45 DENGAN METODE MEAN VARIAN SATU KONSTRAIN

Jurnal Varian ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 22-29
Author(s):  
Gilang Primajati
Keyword(s):  
Capital Markets ◽  
Minimum Variance ◽  
Return Rate ◽  
Optimal Portfolio ◽  
Portfolio Diversification ◽  
Portfolio Risk ◽  
Expected Return ◽  
Positive Weight ◽  
The Mean

In the capital markets, especially the investment market, the establishment of a portfolio is something that must be understood by investors. Portfolio formation by investors to maximize profits as much as possible by minimizing the risk of losses that may occur. Portfolio diversification is defined as portfolio formation in such a way that it can reduce portfolio risk without sacrificing returns. Optimal portfolio with efficient-portfolio mean criteria, investors only invest in risk assets only. Investors do not include risk free assets in their portfolios. The efficient variance portfolio is defined as a portfolio that has minimum variance among the overall possible portfolio that can be formed, at the same expected return rate. The mean method of one constraint variant can be used as the basis for optimal portfolio determination. The shares of LQ-45 used are shares of AALI, BBCA, UNVR, TLKM and ADHI. AALI shares received a positive weight of 7%, BBCA 48%, UNVR 16%, TLKM 26% and ADHI 3%

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