scholarly journals Convex hull estimation of mammalian body segment parameters

2021 ◽  
Vol 8 (6) ◽  
pp. 210836
Author(s):  
Samuel J. Coatham ◽  
William I. Sellers ◽  
Thomas A. Püschel
Keyword(s):  
Soft Tissue ◽  
Convex Hull ◽  
Least Squares ◽  
Soft Tissues ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Regression Equations ◽  
Body Segment ◽  
Extant Species ◽  
Body Segment Parameters

Obtaining accurate values for body segment parameters (BSPs) is fundamental in many biomechanical studies, particularly for gait analysis. Convex hulling, where the smallest-possible convex object that surrounds a set of points is calculated, has been suggested as an effective and time-efficient method to estimate these parameters in extinct animals, where soft tissues are rarely preserved. We investigated the effectiveness of convex hull BSP estimation in a range of extant mammals, to inform the potential future usage of this technique with extinct taxa. Computed tomography scans of both the skeleton and skin of every species investigated were virtually segmented. BSPs (the mass, position of the centre of mass and inertial tensors of each segment) were calculated from the resultant soft tissue segments, while the bone segments were used as the basis for convex hull reconstructions. We performed phylogenetic generalized least squares and ordinary least squares regressions to compare the BSPs calculated from soft tissue segments with those estimated using convex hulls, finding consistent predictive relationships for each body segment. The resultant regression equations can, therefore, be used with confidence in future volumetric reconstruction and biomechanical analyses of mammals, in both extinct and extant species where such data may not be available.

REDUNDANCY OF MOMENT CONDITIONS AND THE EFFICIENCY OF OLS IN SUR MODELS

2008 ◽  
Vol 24 (5) ◽  
pp. 1456-1460 ◽  
Author(s):  
Hailong Qian
Keyword(s):  
Least Squares ◽  
Asymptotic Efficiency ◽  
Generalized Method Of Moments ◽  
Sufficient Conditions ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
American Statistical Association ◽  
Orthogonality Condition ◽  
Moment Conditions ◽  
Necessary And Sufficient

In this note, based on the generalized method of moments (GMM) interpretation of the usual ordinary least squares (OLS) and feasible generalized least squares (FGLS) estimators of seemingly unrelated regressions (SUR) models, we show that the OLS estimator is asymptotically as efficient as the FGLS estimator if and only if the cross-equation orthogonality condition is redundant given the within-equation orthogonality condition. Using the condition for redundancy of moment conditions of Breusch, Qian, Schmidt, and Wyhowski (1999, Journal of Econometrics 99, 89–111), we then derive the necessary and sufficient condition for the equal asymptotic efficiency of the OLS and FGLS estimators of SUR models. We also provide several useful sufficient conditions for the equal asymptotic efficiency of OLS and FGLS estimators that can be interpreted as various mixings of the two famous sufficient conditions of Zellner (1962, Journal of the American Statistical Association 57, 348–368).

scholarly journals The effect of creative corporate culture and intangibility on the performance of foreign firms traded on the NYSE

2018 ◽  
Vol 15 (4) ◽  
pp. 356-372 ◽  
Author(s):  
Marcia Martins Mendes De Luca ◽  
Paulo Henrique Nobre Parente ◽  
Emanoel Mamede Sousa Silva ◽  
Ravena Rodrigues Sousa
Keyword(s):  
New York ◽  
Least Squares ◽  
Corporate Culture ◽  
Corporate Performance ◽  
Stock Exchange ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Foreign Firms ◽  
Content Type ◽  
Negative Effect

Purpose Following the tenets of resource-based view, the present study aims to investigate the effect of creative corporate culture according to the competing values framework model at the level of corporate intangibility and its respective repercussions on performance. Design/methodology/approach The sample included 117 non-USA foreign firms traded on the New York Stock Exchange (NYSE), which issued annual financial reports between 2009 and 2014 using the 20-F form. To meet the study objectives, in addition to the descriptive and comparative analyses, the authors performed regression analyses with panel data, estimating generalized least-squares, two-stage least-squares and ordinary least-squares. Findings Creative culture had a negative effect on the level of intangibility and corporate performance, while the level of intangibility did not appear to influence corporate performance. When combined, creative culture and intangibility had a potentially negative effect on corporate results. In conclusion, creative corporate culture had a negative effect on performance, even in firms with higher levels of intangibility, characterized by elements like experimentation and innovation. Originality/value Although the study hypotheses were eventually rejected, the analyses are relevant to both the academic setting and the market because of the organizational and institutional aspects evaluated, especially in relation to intangibility and creative culture and in view of the unique cross-cultural approach adopted. Within the corporate setting, the study provides a spectrum of stakeholders with tools to identify the profile of foreign firms traded on the NYSE.

Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models

1996 ◽  
Vol 6 ◽  
pp. 1-36 ◽  
Author(s):  
Nathaniel Beck ◽  
Jonathan N. Katz
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Cross Section ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Correlated Errors ◽  
Section Data ◽  
Variable Approach ◽  
Parameter Heterogeneity ◽  
Lagged Dependent Variable

In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series-cross-section models. In this article we compare our proposed method with another leading technique, Kmenta's “cross-sectionally heteroskedastic and timewise autocorrelated” model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable rather than via serially correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series-cross-section data.

ASYMPTOTIC EFFICIENCY OF THE ORDINARY LEAST SQUARES ESTIMATOR FOR REGRESSIONS WITH UNSTABLE REGRESSORS

2002 ◽  
Vol 18 (5) ◽  
pp. 1121-1138 ◽  
Author(s):  
DONG WAN SHIN ◽  
MAN SUK OH
Keyword(s):  
Least Squares ◽  
Asymptotic Efficiency ◽  
Characteristic Root ◽  
Sufficient Conditions ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Least Squares Estimator ◽  
Characteristic Roots ◽  
Generalized Least Squares Estimator ◽  
Ordinary Least Squares Estimator

For regression models with general unstable regressors having characteristic roots on the unit circle and general stationary errors independent of the regressors, sufficient conditions are investigated under which the ordinary least squares estimator (OLSE) is asymptotically efficient in that it has the same limiting distribution as the generalized least squares estimator (GLSE) under the same normalization. A key condition for the asymptotic efficiency of the OLSE is that one multiplicity of a characteristic root of the regressor process is strictly greater than the multiplicities of the other roots. Under this condition, the covariance matrix Γ of the errors and the regressor matrix X are shown to satisfy a relationship (ΓX = XC + V for some matrix C) for V asymptotically dominated by X, which is analogous to the condition (ΓX = XC for some matrix C) for numerical equivalence of the OLSE and the GLSE.

Harmonizing biomass tables by generalized least squares

1985 ◽  
Vol 15 (2) ◽  
pp. 331-340 ◽  
Author(s):  
T. Cunia ◽  
R. D. Briggs
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Regression Function ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Sample Tree ◽  
Tree Data ◽  
Regression Techniques ◽  
Ordinary Least Squares Method

To construct biomass tables for various tree components that are consistent with each other, one may use linear regression techniques with dummy variables. When the biomass of these components is measured on the same sample trees, one should also use the generalized rather than ordinary least squares method. A procedure is shown which allows the estimation of the covariance matrix of the sample biomass values and circumvents the problem of storing and inverting large covariance matrices. Applied to 20 sets of sample tree data, the generalized least squares regressions generated estimates which, on the average were slightly higher (about 1%) than the sample data. The confidence and prediction bands about the regression function were wider, sometimes considerably wider than those estimated by the ordinary weighted least squares.

scholarly journals Optimal data processing in the ballistic laser gravimeter under the effect of correlated interference

2021 ◽  
pp. 10-16
Author(s):  
Anatolii Omelchenko ◽  
Oleksandr Vinnichenko ◽  
Pavel Neyezhmakov ◽  
Oleksii Fedorov ◽  
Volodymyr Bolyuh
Keyword(s):  
Mathematical Model ◽  
Data Processing ◽  
Least Squares ◽  
Measurement Method ◽  
Weighted Least Squares ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Gravitational Acceleration ◽  
Time Data ◽  
Hahn Polynomials

Abstract In order to develop optimal data processing algorithms in ballistic laser gravimeters under the effect of correlated interference, the method of generalized least squares is applied. In this case, to describe the interference, a mathematical model of the autoregression process is used, for which the inverse correlation matrix has a band type and is expressed through the values of the autoregression coefficients. To convert the “path-time” data from the output of the coincidence circuit of ballistic laser gravimeters to a process uniform in time, their local quadratic interpolation is used. Algorithms for data processing in a ballistic gravimeter, developed on the basis of a method of weighted least squares using orthogonal Hahn polynomials, are considered. To implement a symmetric measurement method, the symmetric Hahn polynomials, characterized by one parameter, are used. The method of mathematical modelling is used to study the gain in the accuracy of measuring the gravitational acceleration by the synthesized algorithms in comparison with the algorithm based on the method of least squares. It is shown that auto seismic interference in ballistic laser gravimeters with a symmetric measurement method can be significantly reduced by using a mathematical model of the second-order autoregressive process in the method of generalized least squares. A comparative analysis of the characteristics of the algorithms developed using the method of generalized least squares, the method of weighted least squares and the method of ordinary least squares is carried out.

scholarly journals Effect of exchange rate on trade of Afghanistan with its trade partners

2020 ◽  
pp. 9-23
Keyword(s):  
Exchange Rate ◽  
Least Squares ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Trade Partner ◽  
Independent Variable ◽  
The Exchange Rate ◽  
Trade Partners ◽  
India And China

The exchange rate is one of the most significant variables in the determination of export or import amount, and its shifts cause decrease and increase in the amount of foreign trade, so it is a prominent economic variable for trade policymaking in developing countries. This topic seeks to investigate the effect of exchange rate on Afghanistan’s do business with its partners that includes: Iran, Pakistan, India, and China. Data collected monthly from 2015 through 2017; and also time serious data used for analyses that there are one dependent variable and one independent variable. The ordinary least-squares (OLS) and generalized least squares (GLS) methods estimate 16 models. Results show that the exchange rate of trade partner of Afghanistan has insignificant effects on Afghanistan trade or Afghanistan exchange rate show unimportant effects on Afghanistan trade partners. In some models which coefficients are significant, the R2 is so small, and it shows a low level of explaining. 4 models are useful among the 16 models. Finally, we can say that the exchange rate of Afghanistan and its trade partners don’t affect the trade amount.

scholarly journals A Combination Method for Averaging OLS and GLS Estimators

Econometrics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 38
Author(s):  
Qingfeng Liu ◽  
Andrey L. Vasnev
Keyword(s):  
Least Squares ◽  
Error Term ◽  
Model Averaging ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Information Criteria ◽  
Combination Method ◽  
Estimation Accuracy ◽  
Regularity Conditions ◽  
Optimal Weights

To avoid the risk of misspecification between homoscedastic and heteroscedastic models, we propose a combination method based on ordinary least-squares (OLS) and generalized least-squares (GLS) model-averaging estimators. To select optimal weights for the combination, we suggest two information criteria and propose feasible versions that work even when the variance-covariance matrix is unknown. The optimality of the method is proven under some regularity conditions. The results of a Monte Carlo simulation demonstrate that the method is adaptive in the sense that it achieves almost the same estimation accuracy as if the homoscedasticity or heteroscedasticity of the error term were known.

The determinants of the hotel sector revenues: The case of French Polynesia

2019 ◽  
Vol 26 (5) ◽  
pp. 809-829
Author(s):  
Gil Montant
Keyword(s):  
Least Squares ◽  
Generalized Least Squares ◽  
French Polynesia ◽  
Ordinary Least Squares ◽  
Panel Regression ◽  
Geographical Distance ◽  
Explanatory Variables ◽  
Basic Model ◽  
Hotel Sector ◽  
The Impact

This article is an empirical analysis focused on the hotel sector in French Polynesia in 2007–2017. One assesses the impact of a set of variables on the French Polynesian hotel sector monthly revenues through a gravity model. First, one specifies a basic model that embeds several potential explanatory variables (the exchange rate (both nominal and real), the rate of unemployment, the geographical distance, some specific historical events, etc.). Next, a second model is specified so as to assess the impact of hotel capacities measured by the number of bedrooms offered. Estimates rest on an unbalanced monthly panel database that embeds main countries from where tourists present in French Polynesia are originated. In order to compare results, each specification is estimated by two methods: classical panel regression (Ordinary least squares /Generalized least squares) and pseudo Poisson maximum likelihood. Both methods lead to coherent results.

scholarly journals Time Scale in Least Square Method

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Özgür Yeniay ◽  
Öznur İşçi ◽  
Atilla Göktaş ◽  
M. Niyazi Çankaya
Keyword(s):  
Least Squares ◽  
Time Scale ◽  
Least Squares Method ◽  
Least Square Method ◽  
Forward Direction ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Regression Equations ◽  
Nabla Derivative ◽  
Derivative Concept

Study of dynamic equations in time scale is a new area in mathematics. Time scale tries to build a bridge between real numbers and integers. Two derivatives in time scale have been introduced and called as delta and nabla derivative. Delta derivative concept is defined as forward direction, and nabla derivative concept is defined as backward direction. Within the scope of this study, we consider the method of obtaining parameters of regression equation of integer values through time scale. Therefore, we implemented least squares method according to derivative definition of time scale and obtained coefficients related to the model. Here, there exist two coefficients originating from forward and backward jump operators relevant to the same model, which are different from each other. Occurrence of such a situation is equal to total number of values of vertical deviation between regression equations and observation values of forward and backward jump operators divided by two. We also estimated coefficients for the model using ordinary least squares method. As a result, we made an introduction to least squares method on time scale. We think that time scale theory would be a new vision in least square especially when assumptions of linear regression are violated.

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