statsmodels.genmod.families.family.Gaussian.resid_anscombe Gaussian.resid_anscombe(endog, mu) [source] The Anscombe residuals for the Gaussian exponential family distribution Parameters: endog : array Endogenous response variable mu : array Fitted mean response variable Returns: resid_anscombe : array The Anscombe residuals for the Gaussian family defined below Notes resid_anscombe = endog - mu

statsmodels.sandbox.distributions.transformed.LogTransf_gen.pdf LogTransf_gen.pdf(x, *args, **kwds) Probability density function at x of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: pdf : ndarray Probability density

statsmodels.genmod.generalized_estimating_equations.GEEResults.cov_params GEEResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column :

statsmodels.sandbox.distributions.extras.ACSkewT_gen.ppf ACSkewT_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: x : array_like quan

statsmodels.tsa.arima_model.ARIMAResults.save ARIMAResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_data is

statsmodels.tsa.filters.filtertools.recursive_filter statsmodels.tsa.filters.filtertools.recursive_filter(x, ar_coeff, init=None) [source] Autoregressive, or recursive, filtering. Parameters: x : array-like Time-series data. Should be 1d or n x 1. ar_coeff : array-like AR coefficients in reverse time order. See Notes init : array-like Initial values of the time-series prior to the first value of y. The default is zero. Returns: y : array Filtered array, number of columns determined

statsmodels.nonparametric.kernel_density.KDEMultivariate.imse KDEMultivariate.imse(bw) [source] Returns the Integrated Mean Square Error for the unconditional KDE. Parameters: bw: array_like : The bandwidth parameter(s). Returns: CV: float : The cross-validation objective function. Notes See p. 27 in [R6] for details on how to handle the multivariate estimation with mixed data types see p.6 in [R7]. The formula for the cross-validation objective function is: Where is the multivar

statsmodels.tsa.vector_ar.var_model.FEVD.summary FEVD.summary() [source]

statsmodels.tsa.filters.filtertools.fftconvolve3 statsmodels.tsa.filters.filtertools.fftconvolve3(in1, in2=None, in3=None, mode='full') [source] Convolve two N-dimensional arrays using FFT. See convolve. for use with arma (old version: in1=num in2=den in3=data better for consistency with other functions in1=data in2=num in3=den note in2 and in3 need to have consistent dimension/shape since I?m using max of in2, in3 shapes and not the sum copied from scipy.signal.signaltools, but here used to

statsmodels.tsa.arima_process.arma2ar statsmodels.tsa.arima_process.arma2ar(ar, ma, nobs=100) [source] get the AR representation of an ARMA process Parameters: ar : array_like, 1d auto regressive lag polynomial ma : array_like, 1d moving average lag polynomial nobs : int number of observations to calculate Returns: ar : array, 1d coefficients of AR lag polynomial with nobs elements ` : Notes This is just an alias for ar_representation = arma_impulse_response(ma, ar, nobs=100) ful