H I FREE-BOUND EMISSION OF PLANETARY NEBULAE WITH LARGE ABUNDANCE DISCREPANCIES: TWO-COMPONENT MODELS VERSUS κ-DISTRIBUTED ELECTRONS

Commonly used two-component model conversion formulas that estimate relative body fat (%BF) from body density (Db) were cross-validated on a heterogeneous sample of black men ( n = 30; age = 19–45 yr). A four-component model was used to obtain criterion measures of %BF, and linear regression and analysis of individual residual scores were conducted to assess the predictive accuracy of the formulas under investigation. The two-component formula commonly used to estimate %BF of black men (Schutte JE, Townsend EJ, Hugg J, Shoup RF, Malina RM, and Blomqvist CG. J Appl Physiol 56: 1647–1649, 1984) significantly ( P ≤ 0.01) and systematically (87% of sample) overestimated %BF (−1.28%); thus we developed the following two-component Db conversion formula: %BF = [(4.858/Db) − 4.394] × 100. Because our formula was derived from a four-component model and a larger, more heterogeneous sample than the commonly used two-component formula, we recommend using it to convert Db to %BF for black men. Additionally, there was good agreement between dual-energy X-ray absorptiometry and the four-component model, making this a suitable alternative for estimating the %BF of black men.

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Cross-correlation methods for surface photometry: two-component models

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/256.4.673 ◽

1992 ◽

Vol 256 (4) ◽

pp. 673-678 ◽

Cited By ~ 3

Author(s):

S. Phillipps ◽

P. J. Boyce

Keyword(s):

Cross Correlation ◽

Surface Photometry ◽

Correlation Methods ◽

Two Component ◽

Component Models

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Analysis of Pole Position from 1846 to 1970

Symposium - International Astronomical Union ◽

10.1017/s0074180900098016 ◽

1972 ◽

Vol 48 ◽

pp. 19-32 ◽

Cited By ~ 6

Author(s):

E. M. Gaposchkin

Keyword(s):

Data Analysis ◽

Historical Data ◽

Pole Position ◽

Point Of View ◽

Two Component ◽

Component Motion ◽

Component Models

One- and two-component models for the Chandler motion are investigated with the use of historical data. Evidence for a two-component motion is more convincing from both the data-analysis and the geophysical point of view.

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Statistical Analysis of Price Volatility of Agricultural Commodities Traded at the Ethiopian Commodity Exchange (ECX) Using Multiplicative GARCH-MIDAS Two-component Model

Global Business Review ◽

10.1177/0972150919895628 ◽

2020 ◽

pp. 097215091989562

Author(s):

Teshome Hailemeskel Abebe ◽

Emmanuel Gabreyohannes Woldesenbet ◽

Belaineh Legesse Zeleke

Keyword(s):

Price Volatility ◽

Absolute Error ◽

Fuel Oil ◽

Component Model ◽

Real Effective Exchange Rate ◽

Time Varying ◽

Data Sampling ◽

Commodity Exchange ◽

Two Component ◽

Component Models

We applied multiplicative GARCH-MIDAS two component models for price return volatility of selected commodities traded at the Ethiopian commodity exchange (ECX). Unlike the ‘traditional’ generalized autoregressive conditional heteroscedasticity (GARCH) family models, GARCH-MIDAS component model can capture the time-varying conditional as well as unconditional volatilities, and accommodates macroeconomic variables observed at different frequencies through mixed interval data sampling (MIDAS) specification. The results of our specification tests revealed the existence of both time-varying conditional and unconditional variance. The fitted GARCH-MIDAS component models showed that realized volatility, inflation rate and fuel oil price have had an increasing effect on the price volatility of the commodities under consideration, while real effective exchange rate (REER) had the opposite effect. Furthermore, mean square error (MSE), mean absolute error (MAE) and Diebold and Mariano (DM) test were used for evaluating and comparing the forecasting ability of GARCH-MIDAS component models against standard GARCH models. The results revealed that GARCH-MIDAS component models outperformed the standard GARCH model for high-frequency data.

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The low-energy cosmic-ray nuclei and their propagation in interstellar space

Canadian Journal of Physics ◽

10.1139/p68-294 ◽

1968 ◽

Vol 46 (10) ◽

pp. S553-S556 ◽

Cited By ~ 2

Author(s):

G. M. Comstock

Keyword(s):

Additional Data ◽

Cosmic Ray ◽

Low Energy ◽

Energy Spectra ◽

Interstellar Space ◽

Loss Measurement ◽

Depletion Depth ◽

Two Component ◽

The University ◽

Component Models

The differential energy spectra of the cosmic-ray nuclei helium, carbon, nitrogen, and oxygen above 30 MeV/nucleon, boron, neon, magnesium, and silicon above 50 MeV/nucleon, and the iron group above 100 MeV/nucleon, measured in October–December 1964 and May–June 1965 by the University of Chicago charged-particle telescope on board the OGO-I satellite (Comstock et al. 1966b), have been corrected to take account of the effective depletion depth of the gold–silicon solid-state detectors used for rate-of-energy-loss measurement. Additional data from October to December 1965 are included. The magnitudes and relative shapes of the spectra deduced by extrapolation to nearby interstellar space place important constraints on the allowed modes of interstellar propagation for these nuclei. Two-component models are shown to account for most of the observed properties of the interstellar cosmic-ray nuclei.

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A simple two-component description of mass segregation for anisotropic globular clusters

Proceedings of the International Astronomical Union ◽

10.1017/s1743921319006707 ◽

2019 ◽

Vol 14 (S351) ◽

pp. 528-531

Author(s):

S. Torniamenti ◽

G. Bertin ◽

P. Bianchini

Keyword(s):

Numerical Simulations ◽

Globular Clusters ◽

Elliptical Galaxies ◽

Dynamical Models ◽

Two Component ◽

Partial Energy ◽

Energy Equipartition ◽

Mass Segregation ◽

The One ◽

Component Models

AbstractAs a result of the slow action of two-body encounters, globular clusters develop mass segregation and attain a condition of only partial energy equipartition even in their central, most relaxed regions. Realistic numerical simulations show that, during the process, a radially-biased anisotropy profile slowly builds up, mimicking that resulting from incomplete violent relaxation. Commonly used dynamical models, such as the one-component King models, cannot describe these properties. Here we show that simple two-component models based on a distribution function originally conceived to describe elliptical galaxies, recently truncated and adapted to the context of globular clusters, can describe in detail what is observed in complex and realistic numerical simulations.

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COMPARISON OF TWO-COMPONENT AND MULTI-COMPONENT MODELS IN ESTIMATING BODY COMPOSITION OF HISPANIC WOMEN

Medicine & Science in Sports & Exercise ◽

10.1249/00005768-199505001-00671 ◽

1995 ◽

Vol 27 (Supplement) ◽

pp. S118 ◽

Cited By ~ 2

Author(s):

V. Heyward ◽

J. Goodman ◽

D. Grant ◽

K. Kessler ◽

P. Kocina ◽

...

Keyword(s):

Body Composition ◽

Hispanic Women ◽

Two Component ◽

Component Models

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TWO-SIGMA: a novel TWO-component SInGle cell Model-based Association method for single-cell RNA-seq data

10.1101/709238 ◽

2019 ◽

Author(s):

Eric Van Buren ◽

Ming Hu ◽

Chen Weng ◽

Fulai Jin ◽

Yan Li ◽

...

Keyword(s):

Regression Model ◽

Single Cell ◽

Cell Model ◽

Random Effect ◽

Mixed Effects ◽

Rna Seq ◽

Single Cell Model ◽

Two Component ◽

Association Method ◽

Component Models

AbstractIn this paper, we develop TWO-SIGMA, a TWO-component SInGle cell Model-based Association method for differential expression (DE) analyses in single-cell RNA-seq (scRNA-seq) data. The first component models the probability of “drop-out” with a mixed-effects logistic regression model and the second component models the (conditional) mean expression with a mixed-effects negative binomial regression model. TWO-SIGMA is extremely flexible in that it: (i) does not require a log-transformation of the outcome, (ii) allows for overdispersed and zero-inflated counts, (iii) accommodates a correlation structure between cells from the same biological sample via random effect terms, (iv) can analyze unbalanced designs (in which the number of cells does not need to be identical for all samples), (v) can control for additional sample-level and cell-level covariates including batch effects, (vi) provides interpretable effect size estimates, and (vii) enables general tests of DE beyond two-group comparisons. To our knowledge, TWO-SIGMA is the only method for analyzing scRNA-seq data that can simultaneously accomplish each of these features. Simulations studies show that TWO-SIGMA outperforms alternative regression-based approaches in both type-I error control and power enhancement when the data contains even moderate within-sample correlation. A real data analysis using pancreas islet single-cells exhibits the flexibility of TWO-SIGMA and demonstrates that incorrectly failing to include random effect terms can have dramatic impacts on scientific conclusions. TWO-SIGMA is implemented in the R package twosigma available at https://github.com/edvanburen/twosigma.

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