정규화 (통계)

  다른 뜻에 대해서는 정규화(normalization) 문서를 참조하십시오.
  다른 뜻에 대해서는 정규화 (통계) 문서를 참조하십시오.

1 개요[ | ]

normalization
正規化
정규화
이름 사용
표준 점수 [math]\displaystyle{ \frac{X - \mu}{\sigma} }[/math] Normalizing errors when population parameters are known. Works well for populations that are normally distributed[1]
스튜던트 t-통계량 [math]\displaystyle{ \frac{\widehat\beta - \beta_0}{\operatorname{s.e.}(\widehat\beta)} }[/math] the departure of the estimated value of a parameter from its hypothesized value, normalized by its standard error.
스튜던트화 잔차 [math]\displaystyle{ \frac{\hat \varepsilon_i}{\hat \sigma_i} = \frac{X_i - \hat \mu_i}{\hat \sigma_i} }[/math] Normalizing residuals when parameters are estimated, particularly across different data points in regression analysis.
표준화 모먼트 [math]\displaystyle{ \frac{\mu_k}{\sigma^k} }[/math] Normalizing moments, using the standard deviation [math]\displaystyle{ \sigma }[/math] as a measure of scale.
변동계수 [math]\displaystyle{ \frac{\sigma}{\mu} }[/math] Normalizing dispersion, using the mean [math]\displaystyle{ \mu }[/math] as a measure of scale, particularly for positive distribution such as the exponential distribution and Poisson distribution.
min-max 피처 스케일링 [math]\displaystyle{ X' = \frac{X - X_{\min}}{X_{\max} - X_{\min}} }[/math] Feature scaling is used to bring all values into the range [0,1]. This is also called unity-based normalization. This can be generalized to restrict the range of values in the dataset between any arbitrary points [math]\displaystyle{ a }[/math] and [math]\displaystyle{ b }[/math], using for example [math]\displaystyle{ X' = a + \frac{\left(X-X_{\min}\right)\left(b-a\right)}{X_{\max} - X_{\min}} }[/math].

2 같이 보기[ | ]

3 참고[ | ]

  1. Freedman, David; Pisani, Robert; Purves, Roger (2007년 2월 20일). 《Statistics: Fourth International Student Edition》 (영어). W.W. Norton & Company. ISBN 9780393930436. 
문서 댓글 ({{ doc_comments.length }})
{{ comment.name }} {{ comment.created | snstime }}