Estimation of Nonorthogonal Problem Using Time Series Dataset


Abstract

Based on presentation of the principles of nonorthogonal problem, we discuss the difference of some approaches. A simple procedure to include the R-squared and Root Mean Square Error (R.M.S.E) is proposed and tested. The results showed that the Partial Least Square Regression provides better predictions due to a small R.M.S.E value.

Keyword. Nonorthogonal, Mean Square, Partial Least Square, R Square.


Table of Content

1.0 Introduction

2.0 The Ordinary Least Square Model

2.3 Ridge Regression (RR).

3.0 Result and Discussion

3.1 Illustrative Example

4.0 Conclusion

4.1 Recommendation

Reference


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APA

BARTHOLOMEW, D. (2018). Estimation of Nonorthogonal Problem Using Time Series Dataset. Afribary. Retrieved from https://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649

MLA 8th

BARTHOLOMEW, DESMOND "Estimation of Nonorthogonal Problem Using Time Series Dataset" Afribary. Afribary, 29 Jan. 2018, https://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649. Accessed 18 Jun. 2024.

MLA7

BARTHOLOMEW, DESMOND . "Estimation of Nonorthogonal Problem Using Time Series Dataset". Afribary, Afribary, 29 Jan. 2018. Web. 18 Jun. 2024. < https://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649 >.

Chicago

BARTHOLOMEW, DESMOND . "Estimation of Nonorthogonal Problem Using Time Series Dataset" Afribary (2018). Accessed June 18, 2024. https://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649