YOSHIO TAKANE
Biographic Data
| ID | 8864052 |
|---|---|
| NAME | YOSHIO TAKANE |
| GIVEN NAMES | YOSHIO |
| FAMILY NAME | TAKANE |
| SIGNATURE | TAKANE Y |
| AFFILIATIONS | McGill University |
| ORCID | 0000-0002-4892-2018 |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 14 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1972 |
| LATEST PUBLICATION YEAR | 2015 |
| H-INDEX | 0 |
Multidimensional Scaling I
Adolescent Gambling and Coping within a Generalized High-risk Behavior Framework
Fuzzy Clusterwise Growth Curve Models via Generalized Estimating Equations
The growth curve model has been a useful tool for the analysis of repeated measures data. However, it is designed for an aggregate-sample analysis based on the assumption that the entire sample of respondents are from a single homogenous population. Thus, this method may not be suitable when heterogeneous subgroups exist in the population with qualitatively distinct patterns of trajectories. In this paper, the growth curve model is generalized to…
Generalized Constrained Canonical Correlation Analysis
A method for generalized constrained canonical correlation analysis (GCCANO) is proposed that incorporates external information on both rows and columns of data matrices. In this method each set of variables is first decomposed into the sum of several submatrices according to the external information, and then canonical correlation analysis is applied to pairs of derived submatrices, one from each set, to explore linear relationships between them…
The learning of first and second person pronouns in English
Although most English-speaking children master the correct use of first and second person pronouns by three years, some children show persistent reversal errors in which they refer to themselves as you and to others as me . Recently, such differences have been attributed to the relative availability of overheard speech during the learning process. The present study tested this proposal with feed-forward neural networks learning these pronouns. Ne…
Choice model analysis of the “pick any/ n ” type of binary data
In the “pick any/ n ” method, subjects are asked to choose any number of items from a list of n items according to some criterion. This kind of data can be analyzed as a special case of either multiple‐choice data or successive categories data where the number of response categories is limited to two. An item response model was proposed for the latter case, which is a combination of an unfolding model and a choice model. The marginal maximum‐like…
On the Relationship between iTem Response Theory and Factor Analysis of Discretized Variables
Equivalence of marginal likelihood of the two-parameter normal ogive model in item response theory (IRT) and factor analysis of dichotomized variables (FA) was formally proved. The basic result on the dichotomous variables was extended to multicategory cases, both ordered and unordered categorical data. Pair comparison data arising from multiple-judgment sampling were discussed as a special case of the unordered categorical data. A taxonomy of da…
Maximum Likelihood Estimation in the Generalized Case of Thurstone's Model of Comparative Judgment
In light of the empirical evidence against simple scalability a generalized case of Thurstone's model of comparative judgment was proposed, in which covariance terms in discriminal processes are not necessarily assumed zero or constant. In order to avoid the indeterminacy (over-parametrization) problem of the model, the covariance matrix was assumed to have a prescribed rank. A parameter estimation procedure based on the maximum likelihood princi…
A Maximum Likelihood Method for Nonmetric Multidimensional Scaling
A maximum likelihood estimation procedure for nonmetric multidimensional scaling (MAXSCAL-1) described in the previous paper (Takane, 1978) is evaluated using both Monte Carlo and real data. Two Monte Carlo studies are designed each with specific objectives. In the first study various aspects (numerical quality of estimates, robustness, etc.) of solutions are examined as functions of the number of replications per tetrad, the number of observatio…
A Maximum Likelihood Method for Nonmetric Multidimensional Scaling
A maximum likelihood estimation procedure is developed for nonmetric multidimensional scaling (MDS) which applies to the situation in which all empirical pairwise orderings of dissimilarities are assumed to be independent. The proposed method, while formulated within Thurstonian framework, does not presuppose initial unidimensional scaling of “observed” distances. Rather, the original nonmetric data (which are the set of empirical ordinal relatio…
Nonmetric Individual Differences Multidimensional Scaling
A new procedure is discussed which fits either the weighted or simple Euclidian model to data that may (a) be defined at either the nominal, ordinal, interval or ratio levels of measurement; (b) have missing observations; (c) be symmetric or asymmetric; (d) be conditional or unconditional; (e) be replicated or unreplicated; and (f) be continuous or discrete. Various special cases of the procedure include the most commonly used individual differen…
A Statistical Procedure for the Latent Profile Model
A maximum likelihood estimation procedure is presented for the latent profile model when the conditional distributions of manifest variates given specific latent classes are assumed to be normal, together with the associated tests of the goodness of fit. By incorporating means of constraining estimated parameters in various ways, an important class of statistical hypotheses, constant and equality, about the structures of latent classes can be tes…
Regions of Significance in Multiple Regression Analysis
There has been frequent confusion about the meaning of the various possible tests of significance in multiple regression, and this has led to discussions of "apparent contradictions" in regression. This paper considers the case of two predictor variables, and figures are obtained which show the regions of significance of joint regression coefficients, regression coefficients considered separately, and the multiple correlation. The intersection of…
Estimation of the Recruitment Latent Class by Least Squares Methods
First the formal relationship between factor analysis and latent class model of latent structure analysis was explicated and thereupon the recruitment latent class was estimated in the same manner that factor scores are estimated in factor analysis. Finally thus obtained results were compared with the one through direct recruitment probability procedure
No prominent works on this page.
Estimation of the Recruitment Latent Class by Least Squares Methods
First the formal relationship between factor analysis and latent class model of latent structure analysis was explicated and thereupon the recruitment latent class was estimated in the same manner that factor scores are estimated in factor analysis. Finally thus obtained results were compared with the one through direct recruitment probability procedure
Regions of Significance in Multiple Regression Analysis
There has been frequent confusion about the meaning of the various possible tests of significance in multiple regression, and this has led to discussions of "apparent contradictions" in regression. This paper considers the case of two predictor variables, and figures are obtained which show the regions of significance of joint regression coefficients, regression coefficients considered separately, and the multiple correlation. The intersection of…
A Statistical Procedure for the Latent Profile Model
A maximum likelihood estimation procedure is presented for the latent profile model when the conditional distributions of manifest variates given specific latent classes are assumed to be normal, together with the associated tests of the goodness of fit. By incorporating means of constraining estimated parameters in various ways, an important class of statistical hypotheses, constant and equality, about the structures of latent classes can be tes…
Nonmetric Individual Differences Multidimensional Scaling
A new procedure is discussed which fits either the weighted or simple Euclidian model to data that may (a) be defined at either the nominal, ordinal, interval or ratio levels of measurement; (b) have missing observations; (c) be symmetric or asymmetric; (d) be conditional or unconditional; (e) be replicated or unreplicated; and (f) be continuous or discrete. Various special cases of the procedure include the most commonly used individual differen…
A Maximum Likelihood Method for Nonmetric Multidimensional Scaling
A maximum likelihood estimation procedure for nonmetric multidimensional scaling (MAXSCAL-1) described in the previous paper (Takane, 1978) is evaluated using both Monte Carlo and real data. Two Monte Carlo studies are designed each with specific objectives. In the first study various aspects (numerical quality of estimates, robustness, etc.) of solutions are examined as functions of the number of replications per tetrad, the number of observatio…
A Maximum Likelihood Method for Nonmetric Multidimensional Scaling
A maximum likelihood estimation procedure is developed for nonmetric multidimensional scaling (MDS) which applies to the situation in which all empirical pairwise orderings of dissimilarities are assumed to be independent. The proposed method, while formulated within Thurstonian framework, does not presuppose initial unidimensional scaling of “observed” distances. Rather, the original nonmetric data (which are the set of empirical ordinal relatio…
Maximum Likelihood Estimation in the Generalized Case of Thurstone's Model of Comparative Judgment
In light of the empirical evidence against simple scalability a generalized case of Thurstone's model of comparative judgment was proposed, in which covariance terms in discriminal processes are not necessarily assumed zero or constant. In order to avoid the indeterminacy (over-parametrization) problem of the model, the covariance matrix was assumed to have a prescribed rank. A parameter estimation procedure based on the maximum likelihood princi…
On the Relationship between iTem Response Theory and Factor Analysis of Discretized Variables
Equivalence of marginal likelihood of the two-parameter normal ogive model in item response theory (IRT) and factor analysis of dichotomized variables (FA) was formally proved. The basic result on the dichotomous variables was extended to multicategory cases, both ordered and unordered categorical data. Pair comparison data arising from multiple-judgment sampling were discussed as a special case of the unordered categorical data. A taxonomy of da…
Choice model analysis of the “pick any/ n ” type of binary data
In the “pick any/ n ” method, subjects are asked to choose any number of items from a list of n items according to some criterion. This kind of data can be analyzed as a special case of either multiple‐choice data or successive categories data where the number of response categories is limited to two. An item response model was proposed for the latter case, which is a combination of an unfolding model and a choice model. The marginal maximum‐like…
The learning of first and second person pronouns in English
Although most English-speaking children master the correct use of first and second person pronouns by three years, some children show persistent reversal errors in which they refer to themselves as you and to others as me . Recently, such differences have been attributed to the relative availability of overheard speech during the learning process. The present study tested this proposal with feed-forward neural networks learning these pronouns. Ne…
Generalized Constrained Canonical Correlation Analysis
A method for generalized constrained canonical correlation analysis (GCCANO) is proposed that incorporates external information on both rows and columns of data matrices. In this method each set of variables is first decomposed into the sum of several submatrices according to the external information, and then canonical correlation analysis is applied to pairs of derived submatrices, one from each set, to explore linear relationships between them…
Adolescent Gambling and Coping within a Generalized High-risk Behavior Framework
Fuzzy Clusterwise Growth Curve Models via Generalized Estimating Equations
The growth curve model has been a useful tool for the analysis of repeated measures data. However, it is designed for an aggregate-sample analysis based on the assumption that the entire sample of respondents are from a single homogenous population. Thus, this method may not be suitable when heterogeneous subgroups exist in the population with qualitatively distinct patterns of trajectories. In this paper, the growth curve model is generalized to…
Multidimensional Scaling I
Mathematics (12 works) · Statistics (12 works) · Computer Science (9 works) · Advanced Statistical Methods and Models (8 works) · Econometrics (6 works) · Artificial Intelligence (4 works) · Multidimensional scaling (4 works) · Scaling (4 works) · Applied Mathematics (3 works) · Artificial Intelligence (3 works)