Library

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Electronic Resource
    Electronic Resource
    A COMPARISON OF VARIABLE REDUCTION TECHNIQUES IN AN ATTITUDINAL INVESTIGATION OF MEAT PRODUCTS (1988)
    Oxford, UK : Blackwell Publishing Ltd
    Journal of sensory studies 3 (1988), S. 0 
    Details
    ISSN: 1745-459X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: In many attitudinal investigations, particularly those involving free-choice profiling, a very large list of variables or features can emerge. Ordination using generalized Procrustes analysis provides a common base for comparing assessors, but the derived configurations are often high-dimensional and difficult to summarize. This problem can be rectified by selecting a small subset of the original set of variables. Methods of variable selection in principal component analysis can be adapted easily for such purposes, but there is no guarantee with these methods that overall data structure is preserved. A recently introduced variable selection procedure that does aim to preserve the data structure as much as possible would seem to be more appropriate. All methods are described and applied to a set of data arising from an attitudinal investigation of meat products. The results indicate that variable selection should be more widely encouraged.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1745-459X.1988.tb00428.x
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    On the null distribution of distance between two groups, using mixed continuous and categorical variables (1984)
    Springer
    Journal of classification 1 (1984), S. 243-253 
    Details
    ISSN: 1432-1343
    Keywords: Distance between groups ; Location model ; Mixed variables ; Monte Carlo methods ; Simulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The location model is a useful tool in parametric analysis of mixed continuous and categorical variables. In this model, the continuous variables are assumed to follow different multivariate normal distributions for each possible combination of categorical variable values. Using this model, a distance between two populations involving mixed variables can be defined. To date, however, no distributional results have been available, against which to assess the outcomes of practical applications of this distance. The null distribution of estimated distance is therefore considered in this paper, for a range of possible situations. No explicit analytical expressions are derived for this distribution, but easily implementable Monte Carlo schemes are described. These are then applied to previously cited examples.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01890125
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Ordination in the presence of group structure, for general multivariate data (1994)
    Springer
    Journal of classification 11 (1994), S. 195-207 
    Details
    ISSN: 1432-1343
    Keywords: Canonical variate analysis ; Categorical and mixed data ; Distances ; Diversity coefficients ; Metric scaling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A low-dimensional representation of multivariate data is often sought when the individuals belong to a set ofa-priori groups and the objective is to highlight between-group variation relative to that within groups. If all the data are continuous then this objective can be achieved by means of canonical variate analysis, but no corresponding technique exists when the data are categorical or mixed continuous and categorical. On the other hand, if there is noa-priori grouping of the individuals, then ordination of any form of data can be achieved by use of metric scaling (principal coordinate analysis). In this paper we consider a simple extension of the latter approach to incorporate grouped data, and discuss to what extent this method can be viewed as a generalization of canonical variate analysis. Some illustrative examples are also provided.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01195679
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    Book reviews (1985)
    Springer
    Journal of classification 2 (1985), S. 277-299 
    Details
    ISSN: 1432-1343
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01908079
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 5
    Electronic Resource
    Electronic Resource
    A comparison between two distance-based discriminant principles (1987)
    Springer
    Journal of classification 4 (1987), S. 73-84 
    Details
    ISSN: 1432-1343
    Keywords: Classification rules ; Discriminant analysis ; Distance between groups ; Divergence between populations ; Influence functions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A distance-based classification procedure suggested by Matusita (1956) has long been available as an alternative to the usual Bayes decision rule. Unsatisfactory features of both approaches when applied to multinomial data led Goldstein and Dillon (1978) to propose a new distance-based principle for classification. We subject the Goldstein/Dillon principle to some theoretical scrutiny by deriving the population classification rules appropriate not only to multinomial data but also to multivariate normal and mixed multinomial/multinormal data. These rules demonstrate equivalence of the Goldstein/Dillon and Matusita approaches for the first two data types, and similar equivalence is conjectured (but not explicitly obtained) for the mixed data case. Implications for sample-based rules are noted.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01890076
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 6
    Electronic Resource
    Electronic Resource
    Between-group analysis with heterogeneous covariance matrices: The common principal component model (1990)
    Springer
    Journal of classification 7 (1990), S. 81-98 
    Details
    ISSN: 1432-1343
    Keywords: Between-group analysis ; Canonical variate analysis ; Common principal component model ; Eigenvalues and eigenvectors ; Matusita distance between populations ; Metric scaling ; Principal component analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Analysis of between-group differences using canonical variates assumes equality of population covariance matrices. Sometimes these matrices are sufficiently different for the null hypothesis of equality to be rejected, but there exist some common features which should be exploited in any analysis. The common principal component model is often suitable in such circumstances, and this model is shown to be appropriate in a practical example. Two methods for between-group analysis are proposed when this model replaces the equal dispersion matrix assumption. One method is by extension of the two-stage approach to canonical variate analysis using sequential principal component analyses as described by Campbell and Atchley (1981). The second method is by definition of a distance function between populations satisfying the common principal component model, followed by metric scaling of the resulting between-populations distance matrix. The two methods are compared with each other and with ordinary canonical variate analysis on the previously introduced data set.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01889705
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 7
    Electronic Resource
    Electronic Resource
    On the use of cross-validation to assess performance in multivariate prediction (2000)
    Springer
    Statistics and computing 10 (2000), S. 209-229 
    Details
    ISSN: 1573-1375
    Keywords: cross-validation ; ridge regression ; partial least squares ; prediction ; assessment of predictive models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We describe a Monte Carlo investigation of a number of variants of cross-validation for the assessment of performance of predictive models, including different values of k in leave-k-out cross-validation, and implementation either in a one-deep or a two-deep fashion. We assume an underlying linear model that is being fitted using either ridge regression or partial least squares, and vary a number of design factors such as sample size n relative to number of variables p, and error variance. The investigation encompasses both the non-singular (i.e. n 〉 p) and the singular (i.e. n ≤ p) cases. The latter is now common in areas such as chemometrics but has as yet received little rigorous investigation. Results of the experiments enable us to reach some definite conclusions and to make some practical recommendations.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008987426876
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 8
    Electronic Resource
    Electronic Resource
    Efficient cross-validation of principal components (1996)
    Springer
    Statistics and computing 6 (1996), S. 177-177 
    Details
    ISSN: 1573-1375
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00162529
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 9
    Electronic Resource
    Electronic Resource
    Simulation study of confidence regions for canonical variate analysis (1991)
    Springer
    Statistics and computing 1 (1991), S. 41-46 
    Details
    ISSN: 1573-1375
    Keywords: Canonical variates ; confidence regions ; inclusion rates ; Monte Carlo ; normal distribution
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Canonical variate analysis often involves the construction of confidence regions round points representing group means in a 2-dimensional plot. Traditionally circles have always been constructed, but some authors have recently advocated ellipses as being more appropriate. This paper describes a Monte Carlo study investigating the effect of a range of factors on the inclusion rates of true population means within both types of region for normal data. The traditional circles do not perform too badly within a restricted range, but they are nearly always under-included. The ellipses usually have higher inclusion rates, and so are often closer to the nominal rate, but are sometimes over-included.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01890835
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 10
    Electronic Resource
    Electronic Resource
    A stopping rule for structure-preserving variable selection (1996)
    Springer
    Statistics and computing 6 (1996), S. 51-56 
    Details
    ISSN: 1573-1375
    Keywords: Principal component analysis ; Procrustes rotation ; singular value decomposition ; variable selection
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A stopping rule is provided for the backward elimination process suggested by Krzanowski (1987a) for selecting variables to preserve data structure. The stopping rule is based on perturbation theory for Procrustes statistics, and a small simulation study verifies its suitability. Some illustrative examples are also provided and discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00161573
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...