Add to ORKGHistograms are a useful but limited way to estimate or visualize the true, underlying density of some observed data with an unknown distribution. Histograms are essentially discontinuous step functions. So, if you believe that observed data is generated by a continuous density-or even a differentiable density-then another histogram-like estimation procedure might be preferableComponente Curricular::Educação Superior::Ciências Exatas e da Terra::Matemátic
There exist many ways to estimate the shape of the underlying density. Generally, we can categorize ...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...
Histograms are a useful but limited way to estimate or visualize the true, underlying density of som...
Histograms are the usual vehicle for representing medium sized data distributions graphically, but t...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
In the field of data analysis, including environmental data, it is important to know the shape of un...
• What are the statistical properties of kernel functions on estimators? • What influence does the s...
Density Estimation, particularly the procedure using Kernel Functions, is fast becoming a crucial ar...
Density Estimation, particularly the procedure using Kernel Functions, is fast becoming a crucial ar...
Consider the simple case of a moving histogram (which is a very simple kernel). The idea is to recal...
We contribute to the study of data binning in density estimation. The particular disadvantage of his...
There are various methods for estimating a density. A group of methods which estimate the density as...
There exist many ways to estimate the shape of the underlying density. Generally, we can categorize ...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...
Histograms are a useful but limited way to estimate or visualize the true, underlying density of som...
Histograms are the usual vehicle for representing medium sized data distributions graphically, but t...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
In the field of data analysis, including environmental data, it is important to know the shape of un...
• What are the statistical properties of kernel functions on estimators? • What influence does the s...
Density Estimation, particularly the procedure using Kernel Functions, is fast becoming a crucial ar...
Density Estimation, particularly the procedure using Kernel Functions, is fast becoming a crucial ar...
Consider the simple case of a moving histogram (which is a very simple kernel). The idea is to recal...
We contribute to the study of data binning in density estimation. The particular disadvantage of his...
There are various methods for estimating a density. A group of methods which estimate the density as...
There exist many ways to estimate the shape of the underlying density. Generally, we can categorize ...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...