A special Jackknife for Multidimensional Scaling

Leeuw, Jan de; Meulman, Jacqueline J.

doi:10.1007/bf01896814

Cited by 41 publications

(32 citation statements)

References 19 publications

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“…We examined the stability of the unfolding solution using a jackknife resampling procedure (de Leeuw and Meulman 1986). The results show that the relative positions of the state points are tightly constrained in the sense that they do not change very much, regardless of small perturbations in the data.…”

Section: Notesmentioning

confidence: 99%

A New Measure of Policy Spending Priorities in the American States

Jacoby

Schneider

2009

Polit. anal.

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In this paper, we develop and test a general measure of policy expenditures in the American states. Our approach is to construct a spatial proximity model of yearly state program spending. The empirical analysis reveals that state spending patterns vary along a clear and readily-interpretable unidimensional continuum which differentiates policies that provide particularized benefits to needy constituencies from policies that provide broader collective goods. Based upon standard evaluative criteria, the variable created from our model possesses some highly desirable characteristics. And, it compares favorably to other measures of state policy activity. The net result is a yearly score for each state which summarizes that state's spending across all major program areas. More generally, we believe that our variable can be interpreted as valid and reliable representational measurement of state policy priorities. In this capacity, it could occupy an important position within models of state politics.

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Section: Notesmentioning

confidence: 99%

A New Measure of Policy Spending Priorities in the American States

Jacoby

Schneider

2009

Polit. anal.

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“…But the model selection issue is obviously an important problem in "exploratory" MDS. If there is no such prior knowledge, we have to rely on pseudo-inferential techniques such as Ramsay (1982) or de Leeuw and Meulman (1986).…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Scaling Using Majorization: SMACOF inR

Leeuw¹,

Mair²

2009

J. Stat. Soft.

422

319

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In this paper we present the methodology of multidimensional scaling problems (MDS) solved by means of the majorization algorithm. The objective function to be minimized is known as stress and functions which majorize stress are elaborated. This strategy to solve MDS problems is called SMACOF and it is implemented in an R package of the same name which is presented in this article. We extend the basic SMACOF theory in terms of configuration constraints, three-way data, unfolding models, and projection of the resulting configurations onto spheres and other quadratic surfaces. Various examples are presented to show the possibilities of the SMACOF approach offered by the corresponding package.

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“…Besides de Leeuw and Meulman [7], Krzanowski [10] also proposes the application of the leave-one-out version of the jackknife within the CMDS framework by deleting, in turn, each row (or column) vector comprising the dissimilarities of object from the input matrix '…”

Section: Sensitivity Analysis In Classical Multidimensional Scalingmentioning

confidence: 99%

“…For MDS methods, however, there is not much literature available on this matter to date, with the exception of de Leeuw and Meulman [7] and more recently Krzanowski [10] who have all given key contributions. Both approaches offered rely on jackknife and the idea of performing a cross-validation process of data, where a number of "artificial" replicates of data are yielded and the stability of results are assessed by performing the same statistical method on these new data.…”

Section: Introductionmentioning

confidence: 98%

Sensitivity Analysis and Robust Approach in Multidimensional Scaling: An Evaluation of Customer Satisfaction

Solaro¹

2010

Quality Technology & Quantitative Management

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Multidimensional scaling (MDS) methods are devoted par excellence to representing a set of objects in a low-dimensional Euclidean space by preserving, as far as possible, the proximities between the objects themselves. Like other data analysis techniques, MDS methods treat a set of data as the entire population of interest. It is therefore important to determine the stability and robustness of the results against possible perturbations or errors present in the data. This issue is particularly important in customer satisfaction analysis, where satisfaction indicators and customer ranking can be heavily influenced by such anomalies. These kinds of problems are specifically addressed in this work. Starting from commonly applied methods, a combination of sensitivity and robust analysis will be proposed. A case study will then be considered.

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A special Jackknife for Multidimensional Scaling

Abstract: Multidimensional scaling, Jackknife, Cross validation, Stability,

Cited by 41 publications

References 19 publications

A New Measure of Policy Spending Priorities in the American States

A New Measure of Policy Spending Priorities in the American States

Multidimensional Scaling Using Majorization: SMACOF inR

Sensitivity Analysis and Robust Approach in Multidimensional Scaling: An Evaluation of Customer Satisfaction

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