Quasi‐fixed Points w.r.t. the Height Growth Curve

Masuyama, Motosaburo

doi:10.1002/bimj.4710300206

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…The curve has found application, for example, in growth populations with confined space or limited nutrients. Masuyama (1979Masuyama ( , 1985Masuyama ( , 1988Masuyama ( , 1991Masuyama ( , 1992, for example, notes that several growth processes including human stature appear to be biphasic, with some individuals demonstrating an initially slower rate of growth accompanied by more rapid growth at later ages and vice versa. As such, the pattern of growth is different than for the more widely used logistic curve, which has a symmetric inflection point.…”

Section: Monte Carlo Examination Of Model Comparisonmentioning

confidence: 99%

Right-sizing statistical models for longitudinal data.

Wood¹,

Steinley²,

Jackson³

2015

Psychological Methods

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Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to “right-size” the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting overly parsimonious models to more complex better fitting alternatives, and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically under-identified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A three-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation/covariation patterns. The orthogonal, free-curve slope-intercept (FCSI) growth model is considered as a general model which includes, as special cases, many models including the Factor Mean model (FM, McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, Hierarchical Linear Models (HLM), Repeated Measures MANOVA, and the Linear Slope Intercept (LinearSI) Growth Model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparison of several candidate parametric growth and chronometric models in a Monte Carlo study.

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Section: Monte Carlo Examination Of Model Comparisonmentioning

confidence: 99%

Right-sizing statistical models for longitudinal data.

Wood¹,

Steinley²,

Jackson³

2015

Psychological Methods

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A Note on the Two‐Compartment Model in Pharmacokinetics

Masuyama

1988

Biometrical J

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Linearized Dentition Plot

Masuyama

1990

Biometrical J

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The dentition is a fundamental procese in life. Then the almost-one parameter hypothesis Ruggests 11s that the individual difference would be represented by one parametar. I n fact, the logarithmically transformed chronological ages Y,i a t the eruption of deciduous teeth D, are represented by R linear equation of its mean Y,.(4), where two coefficients q and bi are highly negatively correlated. It impliea the exiabnce of a quasi-fixed point close to this line. See Fig. 1 and Fig. 2, where this point ia denoted by a cr0.w. Thifi point is iitilixed to forerat the age at t,he siibsequent eriipt.ion.

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Quasi‐fixed Points w.r.t. the Height Growth Curve

Cited by 5 publications

References 3 publications

Right-sizing statistical models for longitudinal data.

Right-sizing statistical models for longitudinal data.

A Note on the Two‐Compartment Model in Pharmacokinetics

Linearized Dentition Plot

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