statsmodels.sandbox.distributions.extras.mvstdnormcdf statsmodels.sandbox.distributions.extras.mvstdnormcdf(lower, upper, corrcoef, **kwds) [source] standardized multivariate normal cumulative distribution function This is a wrapper for scipy.stats.kde.mvn.mvndst which calculates a rectangular integral over a standardized multivariate normal distribution. This function assumes standardized scale, that is the variance in each dimension is one, but correlation can be arbitrary, covariance = co

statsmodels.regression.quantile_regression.QuantRegResults.t_test QuantRegResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple

statsmodels.tsa.arima_model.ARIMAResults.maparams static ARIMAResults.maparams()

statsmodels.discrete.discrete_model.CountModel.information CountModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.linear_model.RegressionResults.wresid static RegressionResults.wresid() [source]

statsmodels.genmod.cov_struct.Independence.covariance_matrix Independence.covariance_matrix(expval, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : The covariance o

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.fit TransfTwo_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape param

statsmodels.discrete.discrete_model.MultinomialModel.cdf MultinomialModel.cdf(X) The cumulative distribution function of the model.

statsmodels.nonparametric.bandwidths.bw_silverman statsmodels.nonparametric.bandwidths.bw_silverman(x, kernel=None) [source] Silverman?s Rule of Thumb Parameters: x : array-like Array for which to get the bandwidth kernel : CustomKernel object Unused Returns: bw : float The estimate of the bandwidth Notes Returns .9 * A * n ** (-1/5.) where A = min(std(x, ddof=1), IQR/1.349) IQR = np.subtract.reduce(np.percentile(x, [75,25])) References Silverman, B.W. (1986) Density Estimation.

statsmodels.regression.mixed_linear_model.MixedLMResults.cov_params MixedLMResults.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 : ar