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Show that the ridge estimator is the solution

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Reference no: EM131479887

Question 1. Show that the ridge estimator is the solution to the problem

Minimizeβ (β - β^)' X'X(β - β^)

subject to ββ' ≤ d2.

Question 2. Show that the least squares estimate of β (say β^(-i) with the ith observation deleted can be written in terms of the estimate based on all n points as

β^(-i) = β^- ei/(1-hii).(X'X)-1xi

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This assignment or paper is about the Linear Regression analysis. It’s emphasis on the ridge estimator. How can we minimize the value of the ridge estimator and in what case? Also in this paper, the least squares estimate in linear regression analysis, this method is about estimating parameters by minimizing the squared discrepancies between observed data. We will study the method in the context of a regression problem. In this, the ß parameter is generalized to the ith times, and take the observation for regression analysis.Therefore, in this work, we deal with the results and some identity of ridge estimator and least square estimate.

Reference no: EM131479887

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