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Pearson’s Correlation
This chapter discusses the essential concepts involved in computing the Pearson’s correlation. Properties of variance, covariance and correlation are...
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Rank Correlation
This chapter discusses the rank correlation and its extensions. The rank vector is introduced in section 1. This is followed by a discussion of...
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Applications of Correlation
This chapter gives a bird’s eye view of application of correlation in various fields. A large number of R packages for this purposes are given in...
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Correlation and Regression
The test procedures introduced across the preceding chapters were tailored to testing difference hypotheses. This chapter turns to the complementary...
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Application of distance standard deviation in functional data analysis
This paper concerns the measurement and testing of equality of variability of functional data. We apply the distance standard deviation constructed...
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Sampling Distribution of Correlation
This chapter discusses the sampling distribution of correlation coefficients. The sampling distribution of covariance under normality is discussed...
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Correlation and Regression Analysis
To investigate the relationship between quantitative variables, the most commonly used statistical techniques are correlation and regression analysis.
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Analysis of Correlation and Regression
It is quite often that one is interested to quantify the dependence (positive or negative) between two or more random variables. The basic role of...
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Correlation and Regression
Correlation and regression are the techniques which are used to investigate if there is a relationship between two quantitative variables....
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FPDclustering: a comprehensive R package for probabilistic distance clustering based methods
Data clustering has a long history and refers to a vast range of models and methods that exploit the ever-more-performing numerical optimization...
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Characteristics of Distance Matrices Based on Euclidean, Manhattan and Hausdorff Coefficients
From n -size samples of k -variate points, we construct n × n distance-matrices based on the widely used Euclidean, Manhattan and Hausdorff...
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Mahalanobis Distance Based K-Means Clustering
In the current era, big data are everywhere. With advances in technology, high volumes of a wide variety of data are generated and collected in...
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A new non-iterative deterministic algorithm for constructing asymptotically orthogonal maximin distance Latin hypercube designs
Latin hypercube designs (LHDs), maximin distance designs (MDDs) and orthogonal designs (ODs) are becoming popular and preferred choices in many areas...
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Model-free feature screening via distance correlation for ultrahigh dimensional survival data
With the explosion of ultrahigh dimensional data in various fields, many sure independent screening methods have been proposed to reduce the...
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The Performance of a Combined Distance Between Time Series
This paper presents the comparison of a proposed measure of dissimilarity between time series (COMB) with three baseline measures. COMB is a convex...
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Identification of representative trees in random forests based on a new tree-based distance measure
In life sciences, random forests are often used to train predictive models. However, gaining any explanatory insight into the mechanics leading to a...
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Correlation Integral for Stationary Gaussian Time Series
The correlation integral of a time series is a normalized coefficient that represents the number of close pairs of points of the series lying in...
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A Probabilistic Unfolding Distance Model with the Variability in Objects
Multidimensional unfolding models have been applied to several data types, for example, 2-mode 2-way proximity data. Of course, extensions of these...
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Benchmarking distance-based partitioning methods for mixed-type data
Clustering mixed-type data, that is, observation by variable data that consist of both continuous and categorical variables poses novel challenges....
