How justify chosing variable in paper?
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Variable selection in research papers is justified based on several criteria and approaches. Researchers argue that certain choices of variables are superior for causal analysis . In proteomics, a Bayesian hierarchical model is proposed to select variables that discriminate between healthy and pathological individuals . In the field of discrete discriminant analysis, variable selection techniques are used to address dimensionality issues and improve classification models . Another approach involves using prior knowledge to guide variable selection and predict their relevance without measuring their value on training instances . In large-scale industrial systems, knowledge-based, data-based, and model-based methods are employed for variable selection . These methods aim to improve prediction accuracy and provide operators with timely information to react to changing operating situations .
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| Papers (5) | Insight |
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| The paper mentions two methods for variable selection: using process knowledge to define variable groups and using correlation analysis to determine the relationship between variables. | |
| The paper justifies choosing variables by using prior knowledge to learn the properties of good variables and guide the selection towards the most relevant dimensions. | |
| The paper justifies variable choice by showing that the chosen variables are effective means to achieve the desired ends of the cognitive inquiry. | |
04 May 2014 | The paper justifies the selection of variables by proposing a variable selection method based on a Bayesian hierarchical model that maximizes the posterior probability. |
01 Jun 2013 | The paper justifies choosing variables in Discrete Discriminant Analysis by discussing variable selection techniques that address the issue of dimensionality and reduce computational cost. |
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