Research Frame work of Missing Data Analysis using Mathematical Models

Authors

  • A. Finny Belwin
  • A. Linda Sherin

DOI:

https://doi.org/10.37591/rrdms.v7i3.2617

Keywords:

Imputation, Knowledge Transfer, missing data, Mathematical Models, Boundedness conditions, data patterns, multivariate data, multiple imputation.

Abstract

Abstract

This article deals with providing an overall framework of missing data analysis using mathematical models. Mathematical models serves at large for missing data imputation. Several algorithms in machine learning techniques like Mean, Median, Standard Deviation, Regression and Naïve Bayesian classifier use Mathematical models for analysis. The performance of above mathematical models has been compared by using correlation statistics analysis gives the imputed values are positively related or negatively related or not related with each other. To evaluate the performance of missing values can be measured by using nonparametric approach to inference on multivariate categorical data, which set the bounds to the data imputation. Based upon the performance of nonparametric approach to inference on multivariate categorical data, the imputed missing values are increasing or decreasing or a bounded monotonic sequence of finite limit and also analyzing that every bounded sequence of missing values has a convergent subsequence. Recognizing the above as a specific Poisson process probability gives an easy and exact way of constructing a practical decision indicator. This comes at the cost of requiring the appropriate boundedness conditions. To evaluate the performance, the standard machine learning repository dataset can be used. This article focuses primarily on how to implement Mathematical models to perform imputation of missing values.

 

Keywords: Imputation, Knowledge Transfer, missing data, Mathematical Models, Boundedness conditions, data patterns, multivariate data, multiple imputation.

Downloads

Published

2021-01-18

Issue

Vol. 7 No. 3 (2020)

Section

Research Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.