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plde (version 0.1.2)

Penalized Log-Density Estimation Using Legendre Polynomials

Description

We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).

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Version

Install

install.packages('plde')

Monthly Downloads

164

Version

0.1.2

License

GPL (>= 2)

Maintainer

JungJun Lee

Last Published

July 1st, 2018

Functions in plde (0.1.2)

min_q_lambda

Minimization of the quadratic approximation to objective function
model_selection

Optimal model selection
fit_plde_sub

Fit plde for a fixed tuning parameter
compute_lambdas

Compute lambda sequence
q_lambda

Compute quadratic approximation objective function
plde

Penalized Log-density Estimation Using Legendre Polynomials
legendre_polynomial

legendre_polynomial
fit_plde

Fit plde for a fixed tuning parameter
soft_thresholding

Soft thresholding operator
basic_values

Compute basic values
update

Update the Legendre polynomial coefficient vector
compute_fitted

compute_fitted