Pattern Recognition Letters
The law of the iterated logarithm for the multivariate nearest neighbor density estimators
Journal of Multivariate Analysis
Extensions of fuzzy aggregation
Fuzzy Sets and Systems
A New Way to Represent the Relative Position between Areal Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supremum preserving upper probabilities
Information Sciences: an International Journal
Generalized Fuzzy Hough Transform for Detecting Arbitrary Shapes in a Vague and Noisy Image
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Using quasi-continuous histograms for fuzzy main motion estimation in video sequence
Fuzzy Sets and Systems
On the granularity of summative kernels
Fuzzy Sets and Systems
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
Random intervals as a model for imprecise information
Fuzzy Sets and Systems
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Computing minimal-volume credible sets using interval analysis; application to bayesian estimation
IEEE Transactions on Signal Processing
An axiomatic approach of the discrete Choquet integral as a tool to aggregate interacting criteria
IEEE Transactions on Fuzzy Systems
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Histograms are very useful for summarizing statistical information associated with a set of observed data. They are one of the most frequently used density estimators due to their ease of implementation and interpretation. However, histograms suffer from a high sensitivity to the choice of both reference interval and bin width. This paper addresses this difficulty by means of a fuzzy partition. We propose a new density estimator based on transferring the counts associated with each cell of the fuzzy partition to any subset of the reference interval. We introduce three different methods of achieving this transfer. The properties of each method are illustrated with a classic real observation set. The density estimator obtained relates to the Parzen-Rosenblatt kernel density estimation technique. In this paper, we only consider the monovariate case with precise and imprecise observations.