statsmodels.robust.norms.LeastSquares.psi LeastSquares.psi(z) [source] The psi function for the least squares estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z

statsmodels.sandbox.regression.try_catdata.groupstatsbin statsmodels.sandbox.regression.try_catdata.groupstatsbin(factors, values) [source] uses np.bincount, assumes factors/labels are integers

statsmodels.robust.norms.Hampel.rho Hampel.rho(z) [source] The robust criterion function for Hampel?s estimator Parameters: z : array-like 1d array Returns: rho : array rho(z) = (1/2.)*z**2 for |z| <= a rho(z) = a*|z| - 1/2.*a**2 for a < |z| <= b rho(z) = a*(c*|z|-(1/2.)*z**2)/(c-b) for b < |z| <= c rho(z) = a*(b + c - a) for |z| > c

statsmodels.genmod.families.links.cauchy.inverse_deriv cauchy.inverse_deriv(z) Derivative of the inverse of the CDF transformation link function Parameters: z : array The inverse of the link function at p Returns: The value of the derivative of the inverse of the logit function :

statsmodels.tsa.stattools.pacf_ols statsmodels.tsa.stattools.pacf_ols(x, nlags=40) [source] Calculate partial autocorrelations Parameters: x : 1d array observations of time series for which pacf is calculated nlags : int Number of lags for which pacf is returned. Lag 0 is not returned. Returns: pacf : 1d array partial autocorrelations, maxlag+1 elements Notes This solves a separate OLS estimation for each desired lag.

statsmodels.tsa.varma_process.VarmaPoly.getisinvertible VarmaPoly.getisinvertible(a=None) [source] check whether the auto-regressive lag-polynomial is stationary Returns: isinvertible : boolean *attaches* : maeigenvalues : complex array eigenvalues sorted by absolute value References formula taken from NAG manual

statsmodels.sandbox.regression.gmm.IVGMM.fitgmm_cu IVGMM.fitgmm_cu(start, optim_method='bfgs', optim_args=None) estimate parameters using continuously updating GMM Parameters: start : array_like starting values for minimization Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin

statsmodels.sandbox.distributions.extras.ACSkewT_gen.freeze ACSkewT_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.