🚀 go-pugleaf

gyan@unixg.ubc.ca (Gyan P. Sinha) writes:

>(1) OPTIMIZATION STRATEGY: ML estimation will require some type of
>non-gradient  technique (?), so how do I get the asymtotic t-stats and
>such? 

@article{TishlerZang,
	author = 	{Asher Tishler and Isreal Zang},
	title = 	{A Maximum Likelihood Method for Piecewise Regression Models with a Continuous Dependent Variable},
	journal = 	AS,
	volume = 	30,
	number = 	2,
	pages = 	{116-124},
	year = 		1981,
	annote = 	{MLE piecewise regression.  Approximates max operator in LF.  Speed.}
}

(This might come with source code).

@article{TishlerZang81,
	author = 	{Asher Tishler and Israel Zang},
	title = 	{A new maximum likelihood algorithm for piecewise regression},
	journal = 	JASA,
	volume = 	76,
	number = 	376,
	pages = 	{980-987},
	month = 	{December},
	year = 		1981,
	annote = 	{regression, splines, piecewise regression, switching regressions}
}

If you write some interesting source, could you post on the net?

>(2) MEANING OF T-STATS: If I am estimating the join point from the
>data itself, does that require some special tweaking on the t-ratios
>to get legitimate tests of significance?

Nothing comes to mind.

>(4) ALTERNATIVES: Can anyone suggest alternatives that would allow for
>similar effects, other than introducing a polynomial in DIFF, of which
>I'm already aware of.

A alternative to polynomials and splines is chebyshev expansion.
-- 
Ajay Shah, (213)749-8133, ajayshah@rcf.usc.edu