Advances in surrogate based modeling, feasibility analysis, and optimization: A review
A Bhosekar, M Ierapetritou - Computers & Chemical Engineering, 2018 - Elsevier
The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …
increasing popularity over past three decades. Due to their ability to exploit the black-box …
Model selection techniques: An overview
In the era of big data, analysts usually explore various statistical models or machine-learning
methods for observed data to facilitate scientific discoveries or gain predictive power …
methods for observed data to facilitate scientific discoveries or gain predictive power …
Model averaging and its use in economics
MFJ Steel - Journal of Economic Literature, 2020 - aeaweb.org
The method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different theories exist, as are …
uncertainty, for example in situations where a large amount of different theories exist, as are …
Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC).
SI Vrieze - Psychological methods, 2012 - psycnet.apa.org
This article reviews the Akaike information criterion (AIC) and the Bayesian information
criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on …
criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on …
Sensitivity and specificity of information criteria
Abstract Information criteria (ICs) based on penalized likelihood, such as Akaike's
information criterion (AIC), the Bayesian information criterion (BIC) and sample-size …
information criterion (AIC), the Bayesian information criterion (BIC) and sample-size …
Cross-validation for selecting a model selection procedure
While there are various model selection methods, an unanswered but important question is
how to select one of them for data at hand. The difficulty is due to that the targeted behaviors …
how to select one of them for data at hand. The difficulty is due to that the targeted behaviors …
[图书][B] Introduction to statistical process control
P Qiu - 2013 - books.google.com
A major tool for quality control and management, statistical process control (SPC) monitors
sequential processes, such as production lines and Internet traffic, to ensure that they work …
sequential processes, such as production lines and Internet traffic, to ensure that they work …
Effect size, statistical power, and sample size requirements for the bootstrap likelihood ratio test in latent class analysis
Selecting the number of different classes that will be assumed to exist in the population is an
important step in latent class analysis (LCA). The bootstrap likelihood ratio test (BLRT) …
important step in latent class analysis (LCA). The bootstrap likelihood ratio test (BLRT) …
Nearly unbiased variable selection under minimax concave penalty
CH Zhang - 2010 - projecteuclid.org
We propose MC+, a fast, continuous, nearly unbiased and accurate method of penalized
variable selection in high-dimensional linear regression. The LASSO is fast and continuous …
variable selection in high-dimensional linear regression. The LASSO is fast and continuous …
How true is grit? Assessing its relations to high school and college students' personality characteristics, self-regulation, engagement, and achievement.
Abstract Duckworth, Peterson, Matthews, and Kelly (2007) defined grit as one's passion and
perseverance toward long-term goals. They proposed that it consists of 2 components …
perseverance toward long-term goals. They proposed that it consists of 2 components …