I\'m a computer science, I am confused when I will implementation this model in

Question

I'm a computer science, I am confused when I will implementation this model in python, I can not find a library (python) that can be used for this model, or anyone have example (implementation of mathematics) for this model? I see the solution in chegg.com using software to resolve this exersise https://www.chegg.com/homework-help/Applied-Multivariate-Statistical-Analysis-6th-edition-chapter-9-problem-20E-solution-9780131877153

I'm capturing this image from Applied Multivariate Statistical Analysis (Richard A. Johnson) on page 529.

thank you for your attention.

I'm a computer science, I am confused when I will implementation this model in python, I can not find a library (python) that can be used for this model, or anyone have example (implementation of mathematics) for this model? I see the solution in chegg.com using software to resolve this exersise https://www.chegg.com/homework-help/Applied-Multivariate-Statistical-Analysis-6th-edition-chapter-9-problem-20E-solution-9780131877153

I'm capturing this image from Applied Multivariate Statistical Analysis (Richard A. Johnson) on page 529.

thank you for your attention.

1. Compute initial estimates of the specific variances y1,W2,..., p. Joreskog 181 suggests setting (9A-4) 2 p) s'i where s" is the ith diagonal element of S Some Computational Details for Maximum Likelihood Estimation 529 2. Given y, compute the first m distinct eigenvalues, A1 A Am 1, and corresponding eigenvectors, e1,e2, ...,em, of the "uniqueness-rescaled" covari ance matrix (9A-5) 1/2 Let E e2 i... i eml be the p x m matrix of normalized eigenvectors and A diagli, A Aml be the m x m diagonal matrix of eigenvalues. From (9A-1), A I+ A and E 1/2 LA 1/2. Thus, we obtain the estimates 1/2 1/2 1/2 (9A-6) 3. Substitute L obtained in (9A-6) into the likelihood function (9A-3), and minimize the result with respect to yi, 2,..., o. A numerical search routine must be used. The values W1, 2, ...,We obtained from this minimization are employed at Step (2) to create a new L. Steps (2) and (3) are repeated until con vergence that is, until the differences between successive values of eu and are negligible.

Explanation / Answer

If you just need the help for libraries,

Here are the libraries that you can use in the code

from pydoc import help
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.metrics as metrics
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from scipy import stats
from IPython.display import display, HTML

Please let me know if this is what you need, else please provide more informationn on your request

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