The structural relationship: regression in biology

1988 ◽  
Vol 66 (11) ◽  
pp. 2329-2339 ◽  
Author(s):  
B. H. McArdle
Keyword(s):  
Least Squares ◽  
Geometric Mean ◽  
Simulated Data ◽  
Major Axis ◽  
Least Square ◽  
Data Sets ◽  
Ordinary Least Square ◽  
Reduced Major Axis ◽  
Ordinary Least Square Regression ◽  
Simulated Data Sets

Most biologists are now aware that ordinary least square regression is not appropriate when the X and Y variables are both subject to random error. When there is no information about their error variances, there is no correct unbiased solution. Although the major axis and reduced major axis (geometric mean) methods are widely recommended for this situation, they make different, equally restrictive assumptions about the error variances. By using simulated data sets that violate these assumptions, the reduced major axis method is shown to be generally more efficient and less biased than the major axis method. It is concluded that if the error rate of the X variable is thought to be more than a third of that on the Y variable, then the reduced major axis method is preferable; otherwise the least squares technique is acceptable. An analogous technique, the standard minor axis method, is described for use in place of least squares multiple regression when all of the variables are subject to error.

scholarly journals Local Meteoric Water Line of Northern Chile (18° S–30° S): An Application of Error-in-Variables Regression to the Oxygen and Hydrogen Stable Isotope Ratio of Precipitation

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 791 ◽  
Author(s):  
Boschetti ◽  
Cifuentes ◽  
Iacumin ◽  
Selmo
Keyword(s):  
Isotope Ratio ◽  
Major Axis ◽  
Stable Isotope Ratio ◽  
Least Square ◽  
Northern Chile ◽  
Weighted Mean ◽  
Ordinary Least Square ◽  
Error In Variables ◽  
Least Square Regression ◽  
Ordinary Least Square Regression

In this study, a revision of the previously published data on hydrogen (2H/1H) and oxygen (18O/16O) stable isotope ratio of precipitation in northern Chile is presented. Using the amount-weighted mean data and the combined standard deviation (related to both the weighted mean calculation and the spectrometric measurement), the equation of the local meteoric line calculated by error-in-variables regression is as follows: Northern Chile EIV-LMWL: δ2H = [(7.93 ± 0.15) δ18O] + [12.3 ± 2.1]. The slope is similar to that obtained by ordinary least square regression or other types of regression methods, whether weighted or not (e.g., reduced major axis or major axis) by the amount of precipitation. However, the error-in-variables regression is more accurate and suitable than ordinary least square regression (and other types of regression models) where statistical assumptions (i.e., no measurement errors in the x-axis) are violated. A generalized interval of δ2H = ±13.1‰ is also proposed to be used with the local meteoric line. This combines the confidence and prediction intervals around the regression line and appears to be a valid tool for distinguishing outliers or water samples with an isotope composition significantly different from local precipitation. The applicative examples for the Pampa del Tamarugal aquifer system, snow samples and the local geothermal waters are discussed.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

Tacit knowledge transfer in coopetition

2017 ◽  
Vol 10 (4) ◽  
pp. 453-468 ◽  
Author(s):  
Amit Kumar ◽  
Swarup Kumar Dutta
Keyword(s):  
Knowledge Transfer ◽  
Tacit Knowledge ◽  
Least Square ◽  
Service Firms ◽  
Content Type ◽  
Ordinary Least Square ◽  
Tacit Knowledge Transfer ◽  
Potential Benefits ◽  
Ordinary Least Square Regression ◽  
Empirical Context

Purpose The purpose of this paper is to understand how firms affiliated to business groups (BGs) are able to improve their innovation capability (IC) when engaged in coopetition (collaboration between competing firms). This study aims to explore the relationship between coopetitive relationship strength (CRS), the extent of tacit knowledge transfer (TKT) and IC as well as examine the moderating effect of both BG affiliation and coopetitive experience. Design/methodology/approach The paper examines inter-firm relationships within the empirical context of Indian manufacturing and service firms, by adopting (ordinary least square) regression analysis to test the various hypotheses. The central thesis is that the TKT in coopetition constitutes an important driver to the IC. Findings The paper provides some evidence that inter-firm CRS influences the extent of TKT, and the extent of TKT affects firm IC. The results support that firms in coopetition gain more if their coopetitive partner has a BG affiliation. In absence of a BG affiliation of any of the coopetitive partners, the buildup of TKT reduces as CRS is increased. Research limitations/implications Additional large-sample of data may attempt to validate relationships. The study, however, did not consider all enablers that are critical for TKT. Despite these limitations, analysis provides important and novel perspectives. Practical implications The paper contributes to develop executives’ practices in understanding potential benefits of coopetitive relationship. The implications of this research are important for managers seeking understanding of the management of coopetition. Originality/value The paper makes a modest attempt to investigate the various scenarios of the presence or absence of the moderation of BGs and its impact on CRS in the buildup of TKT. This is the first attempt to link coopetition to the TKT in the BG literature. This study also contributes to our understanding of coopetition in a non-western context.

Users’ perception of cybersecurity, trust and cloud computing providers’ performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongjiang Xu ◽  
Sakthi Mahenthiran
Keyword(s):  
Cloud Computing ◽  
Structural Equation ◽  
Design Methodology ◽  
Positive Relationship ◽  
Least Square ◽  
Equation Modeling ◽  
Content Type ◽  
Ordinary Least Square ◽  
Ordinary Least Square Regression ◽  
Computing Literature

Purpose This study aims to develop a scale to measure the cloud provider’s performance and it investigates the factors that impact that performance from the users’ perspective. Design/methodology/approach This paper proposes a research framework, develops hypotheses and conducts a survey to test the framework. Findings The results from both ordinary least square regression and structural equation modeling analyzes indicate that information technology complexity negatively and significantly affects users’ perception of the cloud computing providers’ performance. Additionally, the trust in the supervisor significantly enhances the otherwise insignificant positive relationship between providers’ cybersecurity capability and users’ perception of their providers’ performance. Originality/value The research makes important contributions to the cloud computing literature, as it measures users’ perception of the cloud computing provider’s performance and links it with cybersecurity, technical complexity and incorporates both the trust in the client firm’s supervisor and the strength of cybersecurity offered by cloud computing provider.

scholarly journals THE ROLE OF HOUSEHOLD COMPOSITION IN EGG CONSUMPTION IN THE OBAFEMI AWOLOWO UNIVERSITY COMMUNITY

2021 ◽  
Vol 28 (1) ◽  
pp. 98-102
Author(s):  
A. B. AYANWALE ◽  
J. O. AJETOMOBI
Keyword(s):  
Demand Function ◽  
Household Composition ◽  
Least Square ◽  
Function Parameter ◽  
Parameter Estimates ◽  
Ordinary Least Square ◽  
Egg Consumption ◽  
Ordinary Least Square Regression ◽  
University Community

This paper exainîned the role of household composition in egg cunsumption in Obafemi Awolowo University Community. An Ordinary Least Square regression model was used to obtain at-home demand function parameter estimates for egg. Positive and signiflcant relationship was found between quantity of eggs consumed and both household size and the age of children. A 1% increase in each of the variables would cause a 4.68% and 5.71 % increase in egg consumption respectively. The need for education of the household on the importance of egg consumption and keeping an optimum family size was suggested based on the findings of the study.

scholarly journals Benchmarking Statistical Multiple Sequence Alignment

2018 ◽  
Author(s):  
Michael Nute ◽  
Ehsan Saleh ◽  
Tandy Warnow
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structural Alignment ◽  
Estimation Method ◽  
Simulated Data ◽  
Protein Sequences ◽  
Data Sets ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Simulated Data Sets

AbstractThe estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical co-estimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical co-estimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy is dramatically more accurate than the other alignment methods on the simulated data sets, but is among the least accurate on the biological benchmarks. There are several potential causes for this discordance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments; future research is needed to understand the most likely explanation for our observations. multiple sequence alignment, BAli-Phy, protein sequences, structural alignment, homology

scholarly journals THE EFFECT OF FINANCIAL EXPERTISE ON COMPENSATION AND BOARD OF DIRECTORS TURNOVER

2018 ◽  
Vol 2 (1) ◽  
pp. 51-64
Author(s):  
Danar Irianto ◽  
Nuranisa Anugerah
Keyword(s):  
Board Of Directors ◽  
Least Square ◽  
Graduate Degree ◽  
Board Size ◽  
Post Graduate ◽  
Ordinary Least Square ◽  
Least Square Regression ◽  
Financial Expertise ◽  
Ordinary Least Square Regression ◽  
Financial Company

This research aims to explains association between financial expertise of directors to directors compensation and directors turnover of Indonesia non financial company in 2011-2012. Using ordinary least square regression we used four variables to define financial expertise of directors: age of the directors, tenure of the directors, the post-graduate degree (MBA), and the accounting certification (CPA). However, this study found no association financial expertise to compensation and directors turnover. We hope this study can contributes to financial expertise, compensation, and turnover literature. We also provide implications for companies in determining the compensation of directors based on financial expertise. Further research can be improve by add new variabel such as complexcity and board size.

scholarly journals Bayesian Planet Searches for the 10 cm/s Radial Velocity Era

2015 ◽  
Vol 11 (A29A) ◽  
pp. 205-207
Author(s):  
Philip C. Gregory
Keyword(s):  
Radial Velocity ◽  
State Of The Art ◽  
Simulated Data ◽  
Model Parameters ◽  
Data Sets ◽  
Stellar Activity ◽  
Bayesian Fusion ◽  
Multiple State ◽  
Simulated Data Sets ◽  
Apodization Function

AbstractA new apodized Keplerian model is proposed for the analysis of precision radial velocity (RV) data to model both planetary and stellar activity (SA) induced RV signals. A symmetrical Gaussian apodization function with unknown width and center can distinguish planetary signals from SA signals on the basis of the width of the apodization function. The general model for m apodized Keplerian signals also includes a linear regression term between RV and the stellar activity diagnostic In (R'hk), as well as an extra Gaussian noise term with unknown standard deviation. The model parameters are explored using a Bayesian fusion MCMC code. A differential version of the Generalized Lomb-Scargle periodogram provides an additional way of distinguishing SA signals and helps guide the choice of new periods. Sample results are reported for a recent international RV blind challenge which included multiple state of the art simulated data sets supported by a variety of stellar activity diagnostics.

scholarly journals A high-resolution global runoff estimate based on GIS and an empirical runoff coefficient

2020 ◽  
Vol 51 (6) ◽  
pp. 1238-1260
Author(s):  
Shumin Liang ◽  
Richard Greene
Keyword(s):  
High Resolution ◽  
Empirical Formula ◽  
Least Square ◽  
Runoff Coefficient ◽  
Ordinary Least Square ◽  
Runoff Volume ◽  
Total Runoff ◽  
High Resolution Map ◽  
Runoff Depth ◽  
Ordinary Least Square Regression

Abstract This paper reviews 110 years of global runoff estimation. By employing the method of ordinary least square regression on a sample region's runoff coefficient, an empirical formula of a runoff coefficient is calculated for China. Based on this empirical formula applied with a high-resolution grid of precipitation, runoff is calculated resulting in an equally high-resolution map of global runoff using a geographic information system (GIS). The main results are (1) the global total runoff volume is 47,884 km3, (2) the average runoff depth is 359 mm, (3) the interior drainage region's runoff volume is 1,663 km3, and (4) the average runoff depth is 58.4 mm. The results are compared with the results of the existing literature on global runoff. This study emphasizes the importance of runoff and groundwater recharge in arid and semi-arid regions where the estimation value of runoff depth is significantly increased.

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