Results 1  10
of
43
On measuring the distance between histograms
 PATTERN RECOGNITION
, 2002
"... A distance measure between two histograms has applications in feature selection, image indexing and retrieval, pattern classification and clustering, etc. We propose a distance between sets of measurement values as a measure of dissimilarity of two histograms. The proposed measure has the advantage ..."
Abstract

Cited by 67 (7 self)
 Add to MetaCart
over the traditional distance measures regarding the overlap between two distributions; it takes the similarity of the nonoverlapping parts into account as well as that of overlapping parts. We consider three versions of the univariate histogram, corresponding to whether the type of measurement
Univariate Kernel Density Estimation
"... • What are the statistical properties of kernel functions on estimators? • What influence does the shape/scaling of the kernel functions have on the estimators? • How to chose the scaling parameter in practice? • How can kernel smoothing ideas used in making confidence statements? • How do dependenc ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
, solution is the averaged shifted histogram[10], which is an appealing motivation for kernel methods. • Drawback: step functions. • Multivariate histogram. • Histograms are not so sufficient as other kernel estimators in using the data. 3 Why univariate kernel density estimator? • Effective way to show
Univariate and Bivariate Loglinear Models for Discrete Test Score Distributions
, 2000
"... The welldeveloped theory of exponential families of distributions is applied to the problem of fitting the univariate histograms and discrete bivariate frequency distributions that often arise in the analysis of test scores. These models are powerful tools for many forms of parametric data smoothi ..."
Abstract

Cited by 26 (3 self)
 Add to MetaCart
The welldeveloped theory of exponential families of distributions is applied to the problem of fitting the univariate histograms and discrete bivariate frequency distributions that often arise in the analysis of test scores. These models are powerful tools for many forms of parametric data
THE HISTOGRAM: FOR CONTINUOUS DATA
"... Percent tallies associated with midpoint labeled intervals define the basic histogram generated in PROC UNIVARIATE. However, most statistics textbooks display histograms with frequencies and endpoints rather than percents and midpoints. Frequencies are more descriptive, and endpoints are better suit ..."
Abstract
 Add to MetaCart
Percent tallies associated with midpoint labeled intervals define the basic histogram generated in PROC UNIVARIATE. However, most statistics textbooks display histograms with frequencies and endpoints rather than percents and midpoints. Frequencies are more descriptive, and endpoints are better
Shape Preserving Histogram Approximation ∗
"... Abstract. We present a new method for reconstructing the density function underlying a given histogram. First we analyze the univariate case taking the approximating function in a class of quadraticlike splines with variable degrees. For the analogous bivariate problem we introduce a new scheme bas ..."
Abstract
 Add to MetaCart
Abstract. We present a new method for reconstructing the density function underlying a given histogram. First we analyze the univariate case taking the approximating function in a class of quadraticlike splines with variable degrees. For the analogous bivariate problem we introduce a new scheme
Fuzzy histograms and density estimation, in
 SMPS 2006, Third Internat. Workshop on Soft Methods in Probability and Statistics
"... The probability density function is a fundamental concept in statistics. Specifying the density function f of a random variable X on Ω gives a natural description of the distribution of X on the universe Ω. When it cannot be specified, an estimate of this density may be performed by using a sample o ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
of n observations independent and identically distributed (X1,..., Xn) of X. Histogram is the oldest and most widely used density estimator for presentation and exploration of observed univariate data. The construction of a histogram consists in partitioning a given reference interval Ω into p bins Ak
Fuzzy histograms and density estimation
"... The probability density function is a fundamental concept in statistics. Specifying the density function f of a random variable X on Ω gives a natural description of the distribution of X on Ω. When it cannot be specified, an estimate of this density may be performed by using a sample of n observati ..."
Abstract
 Add to MetaCart
observations iid (X1,..., Xn) of X. Histogram is the oldest and most widely used density estimator for presentation and exploration of observed univariate data. The construction of a histogram consists in partitioning a given reference interval Ω into p bins Ak and to count the number Acck of observations
ASYMPTOTIC PROPERTIES OF UNIVARIATE POPULATION KMEANS CLUSTERS
"... Key Words and Phrases: population kmeans clusters; withincluster sums of squares; cluster lengths. Let f be a density function defined on the closed interval [a, b]. The kmeans partition of this interval is defined to be the kpartition with the smallest within cluster sum of squares. The propert ..."
Abstract
 Add to MetaCart
. The properties of this kraeans partition when k becomes large will be obtained in this paper. The results suggest that the kmeans clustering procedure can be used to construct a variablecell histogram estimate of f using a sample of observations taken from f. ^^' ^ 1 1 1983
On Comparison of Clustering Techniques for Histogram PDF
 Estimation, Pattern Recognit. Image Anal
"... —This paper discusses the problem of finding the number of component clusters in graylevel image histograms. These histograms are often modeled using a standard mixture of univariate normal densities. The problem, however, is that the number of components in the mixture is an unknown variable that ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
—This paper discusses the problem of finding the number of component clusters in graylevel image histograms. These histograms are often modeled using a standard mixture of univariate normal densities. The problem, however, is that the number of components in the mixture is an unknown variable
Abstract Are Histograms Giving You Fits? New SAS ® Software for Analyzing Distributions
"... Exploring and modeling the distribution of a data sample is a key step in many applications of statistics and data mining. This presentation will introduce you to software for creating highresolution graphics displays of data distributions, including histograms, probability plots, and quantilequan ..."
Abstract
 Add to MetaCart
Exploring and modeling the distribution of a data sample is a key step in many applications of statistics and data mining. This presentation will introduce you to software for creating highresolution graphics displays of data distributions, including histograms, probability plots, and quantile
Results 1  10
of
43