scholarly journals Discrete minimum variance smoothing of a polynomial plus random noise

1971 ◽  
Vol 35 (3) ◽  
pp. 630-645 ◽  
Author(s):  
William F Trench
Keyword(s):  
Random Noise ◽  
Minimum Variance

A method for direct measurement of specimen noise in HREM images

1993 ◽  
Vol 51 ◽  
pp. 458-459
Author(s):  
Sidnei Paciornik ◽  
Roar Kilaas ◽  
Ulrich Dahmen ◽  
Michael Adrian O'Keefe
Keyword(s):  
Random Noise ◽  
Image Interpretation ◽  
Thin Foil ◽  
High Resolution Electron Microscopy ◽  
Black Dot ◽  
Atomic Column ◽  
Amorphous Oxide ◽  
Resolution Electron ◽  
Imaging Conditions ◽  
Image Simulations

High resolution electron microscopy (HREM) is a primary tool for studying the atomic structure of defects in crystals. However, the quantitative analysis of defect structures is often seriously limited by specimen noise due to contamination or oxide layers on the surfaces of a thin foil.For simple monatomic structures such as fcc or bcc metals observed in directions where the crystal projects into well-separated atomic columns, HREM image interpretation is relatively simple: under weak phase object, Scherzer imaging conditions, each atomic column is imaged as a black dot. Variations in intensity and position of individual image dots can be due to variations in composition or location of atomic columns. Unfortunately, both types of variation may also arise from random noise superimposed on the periodic image due to an amorphous oxide or contamination film on the surfaces of the thin foil. For example, image simulations have shown that a layer of amorphous oxide (random noise) on the surfaces of a thin foil of perfect crystalline Si can lead to significant shifts in image intensities and centroid positions for individual atomic columns.

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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