min_q_lambda

<p><code>min_q_lambda</code> function gives the coefficient vector
(<code>sm$coefficients</code>) updated by the coordinate descent algorithm iteratively
until the quadratic approximation to the objective function convergences.</p>

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).

JungJun Lee

min_q_lambda: Minimization of the quadratic approximation to objective function

Description

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