In probability and statistics, density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population.
A variety of approaches to density estimation are used, including Parzen windows and a range of data clustering techniques.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. New York: Springer, 2001. ISBN 0-387-95284-5. (See Chapter 6.)
D.W. Scott. Multivariate Density Estimation. Theory, Practice and Visualization. New York: Wiley, 1992.
B.W. Silverman. Density Estimation. London: Chapman and Hall, 1986.
In this example, we will construct three densityestimates for "glu" (plasma glucose concentration), one conditional on the presence of diabetes, the second conditional on the absence of diabetes, and the third not conditional on diabetes.
The densityestimates are kernel densityestimates using a Gaussian kernel.
That is, a Gaussian density function is placed at each data point, and the sum of the density functions is computed over the range of the data.
In probability and statistics, densityestimation is the construction of an estimate, based on observed data, of an unobservable underlying probabilitydensity function.
The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population.
A variety of approaches to densityestimation are used, including Parzen windows and a range of data clustering techniques.
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