Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis

1969 ◽  
Vol 31 (2) ◽  
pp. 350-371 ◽  
Author(s):  
J. B. Ramsey
Keyword(s):  
Regression Analysis ◽  
Least Squares ◽  
Least Squares Regression ◽  
Linear Least Squares ◽  
Specification Errors

Non-Linear Least-Squares Regression

2000 ◽  
Vol 77 (5) ◽  
pp. 669 ◽  
Author(s):  
Sidney Young ◽  
Andrzej Wierzbicki
Keyword(s):  
Least Squares ◽  
Least Squares Regression ◽  
Linear Least Squares ◽  
Non Linear

The Optimality of Potential Rescaling Approaches in Land Data Assimilation

2013 ◽  
Vol 14 (2) ◽  
pp. 650-660 ◽  
Author(s):  
M. Tugrul Yilmaz ◽  
Wade T. Crow
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Least Squares ◽  
Optimal Solution ◽  
Least Squares Regression ◽  
Linear Least Squares ◽  
Optimal Analysis ◽  
Land Data Assimilation ◽  
Temporal Statistics ◽  
Hydrological Variables

Abstract It is well known that systematic differences exist between modeled and observed realizations of hydrological variables like soil moisture. Prior to data assimilation, these differences must be removed in order to obtain an optimal analysis. A number of rescaling approaches have been proposed for this purpose. These methods include rescaling techniques based on matching sampled temporal statistics, minimizing the least squares distance between observations and models, and the application of triple collocation. Here, the authors evaluate the optimality and relative performances of these rescaling methods both analytically and numerically and find that a triple collocation–based rescaling method results in an optimal solution, whereas variance matching and linear least squares regression approaches result in only approximations to this optimal solution.

Contribution of Mindfulness to Individuals' Tendency to Believe and Share Social Media Content

2020 ◽  
pp. 858-878
Author(s):  
Peerayuth Charoensukmongkol
Keyword(s):  
College Students ◽  
Social Media ◽  
Regression Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Media Content ◽  
Positive Contribution ◽  
Full Time ◽  
Direct Link ◽  
Least Squares Regression

The objective of this research was to explore the effect of mindfulness on individuals' tendencies to believe social media content and share it without realizing the potential consequences. The sample used in this study comprised 300 participants in Bangkok, Thailand, of whom 157 were full-time employees and 143 were college students. Results from partial least squares regression analysis supports the hypothesis that individuals who exhibit higher levels of mindfulness tend to be skeptical of the validity of information to which they are exposed. In addition, skepticism is linked to a decreased tendency to believe social media content and to share content on social media. The findings further support a direct link between mindfulness and a decreased tendency to share social media content. Overall, these findings confirm the positive contribution of mindfulness as a quality that may allow individuals to question the validity of social media content before they decide to believe it and share it with others.

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