Biomaterial Database

Structural equation modeling with small sample sizes using two-stage ridge least-squares estimation. 2013

Sunho Jung

School of Management, Kyung Hee University, Seoul, South Korea. sunho.jung@khu.ac.kr

In covariance structure analysis, two-stage least-squares (2SLS) estimation has been recommended for use over maximum likelihood estimation when model misspecification is suspected. However, 2SLS often fails to provide stable and accurate solutions, particularly for structural equation models with small samples. To address this issue, a regularized extension of 2SLS is proposed that integrates a ridge type of regularization into 2SLS, thereby enabling the method to effectively handle the small-sample-size problem. Results are then reported of a Monte Carlo study conducted to evaluate the performance of the proposed method, as compared to its nonregularized counterpart. Finally, an application is presented that demonstrates the empirical usefulness of the proposed method.

UI	MeSH Term	Description	Entries
D008954	Models, Biological	Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.	Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D009010	Monte Carlo Method	In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)	Method, Monte Carlo
D012044	Regression Analysis	Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.	Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
D002957	Citrus	A plant genus of the family RUTACEAE. They bear the familiar citrus fruits including oranges, grapefruit, lemons, and limes. There are many hybrids which makes the nomenclature confusing.	Citron Tree,Fruit, Citrus,Lemon Tree,Orange Tree, Bitter,Orange Tree, Mandarin,Orange Tree, Seville,Orange Tree, Sour,Pomelo Tree,Pummelo Tree,Tangerine Tree,Citrus Fruit,Citrus aurantium,Citrus bergamia,Citrus grandis,Citrus hystrix,Citrus limon,Citrus maxima,Citrus medica,Citrus reticulata,Kaffir Lime,Bitter Orange Tree,Bitter Orange Trees,Citron Trees,Citrus aurantiums,Citrus bergamias,Citrus grandi,Citrus hystrices,Citrus medicas,Citrus reticulatas,Lemon Trees,Lime, Kaffir,Mandarin Orange Tree,Mandarin Orange Trees,Orange Trees, Bitter,Orange Trees, Mandarin,Orange Trees, Seville,Orange Trees, Sour,Pomelo Trees,Pummelo Trees,Seville Orange Tree,Seville Orange Trees,Sour Orange Tree,Sour Orange Trees,Tangerine Trees,Tree, Bitter Orange,Tree, Citron,Tree, Lemon,Tree, Mandarin Orange,Tree, Pomelo,Tree, Pummelo,Tree, Seville Orange,Tree, Sour Orange,Tree, Tangerine,Trees, Bitter Orange,Trees, Citron,Trees, Lemon,Trees, Mandarin Orange,Trees, Pomelo,Trees, Pummelo,Trees, Seville Orange,Trees, Sour Orange,Trees, Tangerine,aurantium, Citrus,bergamia, Citrus,hystrices, Citrus,maxima, Citrus,medicas, Citrus,reticulata, Citrus
D001628	Beverages	Liquids that are suitable for drinking. (From Merriam Webster Collegiate Dictionary, 10th ed)	Beverage
D013649	Taste	The ability to detect chemicals through gustatory receptors in the mouth, including those on the TONGUE; the PALATE; the PHARYNX; and the EPIGLOTTIS.	Gustation,Taste Sense,Gustations,Sense, Taste,Senses, Taste,Taste Senses,Tastes
D016013	Likelihood Functions	Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.	Likelihood Ratio Test,Maximum Likelihood Estimates,Estimate, Maximum Likelihood,Estimates, Maximum Likelihood,Function, Likelihood,Functions, Likelihood,Likelihood Function,Maximum Likelihood Estimate,Test, Likelihood Ratio
D016018	Least-Squares Analysis	A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.	Rietveld Refinement,Analysis, Least-Squares,Least Squares,Analyses, Least-Squares,Analysis, Least Squares,Least Squares Analysis,Least-Squares Analyses,Refinement, Rietveld
D018401	Sample Size	The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups. (From Wassertheil-Smoller, Biostatistics and Epidemiology, 1990, p95)	Sample Sizes,Size, Sample,Sizes, Sample