			Home About us Contact

Geometric Average (geometric + average)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	50%

Selected Abstracts

Analysis of b -value calculations in diffusion weighted and diffusion tensor imaging

CONCEPTS IN MAGNETIC RESONANCE, Issue 1 2005
Daniel Güllmar
Abstract Diffusion weighted imaging has opened new diagnostic possibilities by using microscopic diffusion of water molecules as a means of image contrast. The directional dependence of diffusion has led to the development of diffusion tensor imaging, which allows us to characterize microscopic tissue geometry. The link between the measured NMR signal and the self-diffusion tensor is established by the so-called b matrices that depend on the gradient's direction, strength, and timing. However, in the calculation of b -matrix elements, the influence of imaging gradients on each element of the b matrix is often neglected. This may cause errors, which in turn leads to an incorrect extraction of diffusion coefficients. In cases where the imaging gradients are high (high spatial resolution), these errors may be substantial. Using a generic pulsed gradient spin-echo (PGSE) imaging sequence, the effects of neglecting the imaging gradients on the b -matrix calculation are demonstrated. By measuring an isotropic phantom with this sequence it can be analytically as well as experimentally shown that large deviations in single b -matrix elements are generated. These deviations are obtained by applying the diffusion weighting in the readout direction of the imaging dimension in combination with relatively large imaging gradients. The systematic errors can be avoided by a full b -matrix calculation considering all the gradients of the sequence or by generating cross-term free signals using the geometric average of two diffusion weighted images with opposite polarity. The importance of calculating the exact b matrices by the proposed methods is based on the fact that more precise diffusion parameters are obtained for extracting correct property maps, such as fractional anisotropy, volume ratio, or conductivity tensor maps. © 2005 Wiley Periodicals, Inc. Concepts Magn Reson Part A 25A: 53,66, 2005 [source]

Expression and chromosomal organization of mouse meiotic genes

MOLECULAR REPRODUCTION & DEVELOPMENT, Issue 3 2010
Hiba Waldman Ben-Asher
Microarray technology which enables large scale analysis of gene expression and thus comparison between transcriptomes of different cell types, cells undergoing different treatments or cells at different developmental stages has also been used to study the transcriptome involved with spermatogenesis. Many new germ cell-specific genes were determined, and the resulting genes were classified according to different criteria. However, the biological significance of these classifications and their clustering according to developmental transcriptional patterns during spermatogenesis have not yet been addressed. In this study we utilized mouse testicular transcriptome analysis at five distinct post-natal ages (Days 7, 10, 12, 14, and 17), representing distinct meiotic stages, in an attempt to better understand the biological significance of genes clustered into similar expression patterns during this process. Among 790 sequences that showed an expression level change of twofold or more in any of the five key stages that were monitored, relative to the geometric average of all stages, about 40% peaked and about 30% were specifically suppressed at post-natal day 14 (representing the early pachytene stage of spermatocytes), reflecting tight transcriptional regulation at this stage. We also found that each of the six main transcription clusters that were determined was characterized by statistically significant representation of genes related to specific biological processes. Finally, our results indicated that genes important for meiosis are not randomly distributed along the mouse genome but rather preferentially located on specific chromosomes, suggesting for the first time that chromosomal location might be a regulating factor of meiotic gene expression. Mol. Reprod. Dev. 77: 241,248, 2010. © 2009 Wiley-Liss, Inc. [source]

The drift factor in biased futures index pricing models: A new look

THE JOURNAL OF FUTURES MARKETS, Issue 6 2002
W. Brian Barrett
The presence of bias in index futures prices has been investigated in various research studies. Redfield (11) asserted that the U.S. Dollar Index (USDX) futures contract traded on the U.S. Cotton Exchange (now the FINEX division of the New York Board of Trade) could be systematically arbitraged for nontrivial returns because it is expressed in so-called "European terms" (foreign currency units/U.S. dollar). Eytan, Harpaz, and Krull (4) (EHK) developed a theoretical factor using Brownian motion to correct for the European terms and the bias due to the USDX index being expressed as a geometric average. Harpaz, Krull, and Yagil (5) empirically tested the EHK index. They used the historical volatility to proxy the EHK volatility specification. Since 1990, it has become more commonplace to use option-implied volatility for forecasting future volatility. Therefore, we have substituted option implied volatilities into EHK's correction factor and hypothesized that the correction factor is "better" ex ante and therefore should lead to better futures model pricing. We tested this conjecture using twelve contracts from 1995 through 1997 and found that the use of implied volatility did not improve the bias correction over the use of historical volatility. Furthermore, no matter which volatility specification we used, the model futures price appeared to be mis-specified. To investigate further, we added a simple naïve , based on a modification of the adaptive expectations model. Repeating the tests using this naïve "drift" factor, it performed substantially better than the other two specifications. Our conclusion is that there may be a need to take a new look at the drift-factor specification currently in use. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:579,598, 2002 [source]

The Bias of the RSR Estimator and the Accuracy of Some Alternatives

REAL ESTATE ECONOMICS, Issue 1 2002
William N. Goetzmann
This paper analyzes the implications of cross-sectional heteroskedasticity in the repeat sales regression (RSR). RSR estimators are essentially geometric averages of individual asset returns because of the logarithmic transformation of price relatives. We show that the cross-sectional variance of asset returns affects the magnitude of the bias in the average return estimate for each period, while reducing the bias for the surrounding periods. It is not easy to use an approximation method to correct the bias problem. We suggest an unbiased maximum likelihood alternative to the RSR that directly estimates index returns, which we term MLRSR. The unbiased MLRSR estimators are analogous to the RSR estimators but are arithmetic averages of individual asset returns. Simulations show that these estimators are robust to time-varying cross-sectional variance and that the MLRSR may be more accurate than RSR and some alternative methods. [source]