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Relevant bibliographies by topics / COEFFICIENT ESTIMATES

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Academic literature on the topic 'COEFFICIENT ESTIMATES'

Author: Grafiati

Published: 11 September 2023

Last updated: 16 June 2025

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Journal articles on the topic "COEFFICIENT ESTIMATES"

1

Lentz, S. J., K. A. Davis, J. H. Churchill, and T. M. DeCarlo. "Coral Reef Drag Coefficients – Water Depth Dependence." Journal of Physical Oceanography 47, no. 5 (2017): 1061–75. http://dx.doi.org/10.1175/jpo-d-16-0248.1.

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AbstractA major challenge in modeling the circulation over coral reefs is uncertainty in the drag coefficient because existing estimates span two orders of magnitude. Current and pressure measurements from five coral reefs are used to estimate drag coefficients based on depth-average flow, assuming a balance between the cross-reef pressure gradient and the bottom stress. At two sites wind stress is a significant term in the cross-reef momentum balance and is included in estimating the drag coefficient. For the five coral reef sites and a previous laboratory study, estimated drag coefficients increase as the water depth decreases consistent with open channel flow theory. For example, for a typical coral reef hydrodynamic roughness of 5 cm, observational estimates, and the theory indicate that the drag coefficient decreases from 0.4 in 20 cm of water to 0.005 in 10 m of water. Synthesis of results from the new field observations with estimates from previous field and laboratory studies indicate that coral reef drag coefficients range from 0.2 to 0.005 and hydrodynamic roughnesses generally range from 2 to 8 cm. While coral reef drag coefficients depend on factors such as physical roughness and surface waves, a substantial fraction of the scatter in estimates of coral reef drag coefficients is due to variations in water depth.

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2

Zaprawa, Paweł, and Katarzyna Tra̧bka-Wiȩcław. "Estimates of Coefficient Functionals for Functions Convex in the Imaginary-Axis Direction." Symmetry 12, no. 10 (2020): 1736. http://dx.doi.org/10.3390/sym12101736.

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Let C0(h) be a subclass of analytic and close-to-convex functions defined in the open unit disk by the formula Re{(1−z2)f′(z)}>0. In this paper, some coefficient problems for C0(h) are considered. Some properties and bounds of several coefficient functionals for functions belonging to this class are provided. The main aim of this paper is to find estimates of the difference and of sum of successive coefficients, bounds of the sum of the first n coefficients and bounds of the n-th coefficient. The obtained results are used to determine coefficient estimates for both functions convex in the imaginary-axis direction with real coefficients and typically real functions. Moreover, the sum of the first initial coefficients for functions with a positive real part and with a fixed second coefficient is estimated.

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3

Darus, Maslina, and Ajab Akbarally. "Coefficient estimates for Ruscheweyh derivatives." International Journal of Mathematics and Mathematical Sciences 2004, no. 36 (2004): 1937–42. http://dx.doi.org/10.1155/s0161171204309051.

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We consider functionsf, analytic in the unit disc and of the normalized formf(z)=z+∑n=2∞anzn. For functionsf∈R¯δ(β), the class of functions involving the Ruscheweyh derivatives operator, we give sharp upper bounds for the Fekete-Szegö functional|a3−μa22|.

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4

Idrisa, Hamimu, S. A. Kadam, and S. D. Gorantiwar. "Estimation of Crop Coefficients Based on Normalize Difference Vegetation Index." Journal of Agriculture Research and Technology 47, no. 03 (2022): 381–88. http://dx.doi.org/10.56228/jart.2022.47320.

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Crop coefficient is one of the most important parameters used for the estimation of crop evapotranspiration (ETc). Crop coefficient (Kc)-based estimation of crop evapotranspiration is most commonly used methods for irrigation water management. However, crop coefficient approach used for estimation ETc using the generalized crop coefficients mentioned in Irrigation and Drainage Paper No. 56 of the Food and Agricultural Organization of the United Nations can contribute to crop evapotranspiration estimates that are substantially different from actual crop evapotranspiration. The colinear relationship between the crop coefficient curve and a satellitederived Normalized Difference Vegetation Index (NDVI) showed potential for modeling a crop coefficient as a function of the NDVI, which is also one among the methods used for estimation of ETc in irrigation water management. The present study was conducted with objectives to present the techniques and procedures to develop and estimates Kc based on vegetation index (NDVI) extracted from satellite data. The relationships between and NDVI and crop coefficients (Kc) of wheat and chickpea for corresponding months were developed. The regression models developed are: (Kc) NDVI = 6.3268*NDVI-1.4207 for wheat and (Kc) NDVI = 5.7866 * NDVI-1.6699 for chickpea. The models showed strong relationships with R2= 0.86 and R2=0.84 for wheat and chickpea, respectively. The model and techniques to develop and estimate crop coefficients can be used in other regions in the global, and hence estimate crop evapotranspiration. The crop coefficients (Kc) estimated based on NDVI are useful for irrigation scheduling, evaluating irrigation performance, irrigation water management, and estimation of water use efficiency.

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5

Zaprawa, Paweł, Anna Futa, and Magdalena Jastrzębska. "On Coefficient Functionals for Functions with Coefficients Bounded by 1." Mathematics 8, no. 4 (2020): 491. http://dx.doi.org/10.3390/math8040491.

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In this paper, we discuss two well-known coefficient functionals a 2 a 4 - a 3 2 and a 4 - a 2 a 3 . The first one is called the Hankel determinant of order 2. The second one is a special case of Zalcman functional. We consider them for functions in the class Q R ( 1 2 ) of analytic functions with real coefficients which satisfy the condition ( ) f ( z ) z > 1 2 for z in the unit disk Δ . It is known that all coefficients of f ∈ Q R ( 1 2 ) are bounded by 1. We find the upper bound of a 2 a 4 - a 3 2 and the bound of | a 4 - a 2 a 3 | . We also consider a few subclasses of Q R ( 1 2 ) and we estimate the above mentioned functionals. In our research two different methods are applied. The first method connects the coefficients of a function in a given class with coefficients of a corresponding Schwarz function or a function with positive real part. The second method is based on the theorem of formulated by Szapiel. According to this theorem, we can point out the extremal functions in this problem, that is, functions for which equalities in the estimates hold. The obtained estimates significantly extend the results previously established for the discussed classes. They allow to compare the behavior of the coefficient functionals considered in the case of real coefficients and arbitrary coefficients.

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6

Raghunathan, Trivellore E., Paula K. Diehr, and Allen D. Cheadle. "Combining Aggregate and Individual Level Data to Estimate an Individual Level Correlation Coefficient." Journal of Educational and Behavioral Statistics 28, no. 1 (2003): 1–19. http://dx.doi.org/10.3102/10769986028001001.

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Methods are developed that use aggregate data, possibly based on a large number of individuals, and individual level data, from a small fraction of individuals from the same or similar population, to eliminate ecological bias inherent in the analysis of aggregate data. The primary focus is on estimating the individual level correlation coefficient but the proposed methodology can be extended to estimate regression coefficients. Two approaches, the method of moments and the maximum likelihood, are developed for a bivariate distribution, but can be extended to a multivariate distribution. The method of moments develops a corrected estimate of the within-group covariance matrix, which is then used to estimate the individual level correlation and regression coefficients. The second method assumes bivariate normality and maximizes the combined likelihood function based on the two data sets. The maximum likelihood estimates are obtained using the EM-algorithm. A simulation study investigates the repeated sampling properties of these procedures in terms of bias and the mean square error of the point estimates and the actual coverage of the confidence intervals. The maximum likelihood estimates are almost unbiased and the confidence intervals are well calibrated for simulation conditions considered. The method of moments estimates have the same desirable properties for some simulation conditions. Under all conditions, the correlation coefficient between aggregate variables is severely biased as an estimate of the individual level correlation coefficient.

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7

Orhan, Halit, Nihat Yagmur, and Murat Caglar. "Coefficient estimates for Sakaguchi type functions." SARAJEVO JOURNAL OF MATHEMATICS 8, no. 2 (2012): 235–44. http://dx.doi.org/10.5644/sjm.08.2.05.

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8

SAKAR, F. Müge, and H. Özlem GÜNEY. "Coefficient estimates for bi-concave functions." Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics 68, no. 1 (2018): 53–60. http://dx.doi.org/10.31801/cfsuasmas.443600.

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9

Ozawa, Mitsuru. "Coefficient estimates for the class $\Sigma$." Kodai Mathematical Journal 9, no. 1 (1986): 123–33. http://dx.doi.org/10.2996/kmj/1138037155.

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10

Totik, Vilmos. "Coefficient estimates on general compact sets." Publicationes Mathematicae Debrecen 96, no. 1-2 (2020): 131–39. http://dx.doi.org/10.5486/pmd.2020.8631.

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