statsmodels.nonparametric.kde.KDEUnivariate.fit KDEUnivariate.fit(kernel='gau', bw='normal_reference', fft=True, weights=None, gridsize=None, adjust=1, cut=3, clip=(-inf, inf)) [source] Attach the density estimate to the KDEUnivariate class. Parameters: kernel : str The Kernel to be used. Choices are: ?biw? for biweight ?cos? for cosine ?epa? for Epanechnikov ?gau? for Gaussian. ?tri? for triangular ?triw? for triweight ?uni? for uniform bw : str, float The bandwidth to use. Choices are:

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.entropy ExpTransf_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: 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).

statsmodels.robust.norms.Hampel.weights Hampel.weights(z) [source] Hampel weighting function for the IRLS algorithm The psi function scaled by z Parameters: z : array-like 1d array Returns: weights : array weights(z) = 1 for |z| <= a weights(z) = a/|z| for a < |z| <= b weights(z) = a*(c - |z|)/(|z|*(c-b)) for b < |z| <= c weights(z) = 0 for |z| > c

statsmodels.sandbox.distributions.extras.NormExpan_gen.logpdf NormExpan_gen.logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV. This uses a more numerically accurate calculation if available. 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

statsmodels.emplike.descriptive.DescStatMV.ci_corr DescStatMV.ci_corr(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence intervals for the correlation coefficient Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value the upper confidence limit can be. Default is 99% confidence limit assuming normality. lower_bound : float Minimum value the lower condidence limit can be. Default is 99% confidence limit assuming normal

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.logpdf ExpTransf_gen.logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV. This uses a more numerically accurate calculation if available. 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, opti

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.interval ExpTransf_gen.interval(alpha, *args, **kwds) Confidence interval with equal areas around the median. Parameters: alpha : array_like of float Probability that an rv will be drawn from the returned range. Each value should be in the range [0, 1]. arg1, arg2, ... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional location parame

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.cdf ExpTransf_gen.cdf(x, *args, **kwds) Cumulative distribution function 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: cdf : ndarray Cumulative distributi