Empirical Likelihood(emplike) method in the StatsModels library

This recipe describes what is Empirical Likelihood emplike method in the StatsModels library

Recipe Objective - What is Empirical Likelihood (emplike) method in the StatsModels library?

Empirical probabilities are a method of nonparametric reasoning and estimation that eliminates the need to provide a family of underlying distributions. Moreover, the empirical probabilistic method does not require resampling, but it still clearly determines the confidence range, the shape of which reflects the shape of the data. In essence, empirical probabilities try to combine the advantages of parametric and nonparametric methods while limiting their shortcomings. The main problem with empirical probabilities is the computationally intensive method needed to perform inference. statsmodels.emplike seeks to provide a user-friendly interface that allows end-users to effectively perform empirical probability analysis without worrying about computational overhead. emplike now provides a way to perform hypothesis tests and form confidence intervals for descriptive statistics. Regression accelerated downtime, and empirical probability estimation and inference in instrumental variable models are currently under development.

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Example:

# Importing libraries
import numpy as np
import statsmodels.api as sm

# Generating data
data = np.random.randn(10)

# Applying emplike method
model = sm.emplike.DescStat(data)

# confidence interval for the mean
print(model.ci_mean())

Output-
(-1.2636327993122782, 0.2569613083488486)

In this way, we can use emplike method in the statsmodels library.

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