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imputation    音標拼音: [,ɪmpjət'eʃən]
n. 歸罪,負責,責難

歸罪,負責,責難

imputation
n 1: a statement attributing something dishonest (especially a
criminal offense); "he denied the imputation"
2: the attribution to a source or cause; "the imputation that my
success was due to nepotism meant that I was not taken
seriously"

Imputation \Im`pu*ta"tion\, [L. imputatio an account, a charge:
cf. F. imputation.]
[1913 Webster]
1. The act of imputing or charging; attribution; ascription;
also, anything imputed or charged.
[1913 Webster]

Shylock. Antonio is a good man.
Bassanio. Have you heard any imputation to the
contrary? --Shak.
[1913 Webster]

If I had a suit to Master Shallow, I would humor his
men with the imputation of being near their master.
--Shak.
[1913 Webster]

2. Charge or attribution of evil; censure; reproach;
insinuation.
[1913 Webster]

Let us be careful to guard ourselves against these
groundless imputation of our enemies. --Addison.
[1913 Webster]

3. (Theol.) A setting of something to the account of; the
attribution of personal guilt or personal righteousness of
another; as, the imputation of the sin of Adam, or the
righteousness of Christ.
[1913 Webster]

4. Opinion; intimation; hint.
[1913 Webster]

130 Moby Thesaurus words for "imputation":
accounting for, accusal, accusation, accusing, adverse criticism,
allegation, allegement, animadversion, answerability, application,
arraignment, arrogation, ascription, aspersion, assignation,
assignment, attachment, attaint, attribution, bad notices,
bad press, badge of infamy, bar sinister, baton, bend sinister,
bill of particulars, black eye, black mark, blame, blot, blur,
brand, bringing of charges, bringing to book, broad arrow,
captiousness, carping, cavil, caviling, censoriousness, censure,
challenge, champain, charge, complaint, connection with, count,
credit, criticism, delation, denouncement, denunciation,
derivation from, disparagement, etiology, exception, faultfinding,
flak, hairsplitting, hit, home thrust, honor, hostile criticism,
hypercriticalness, hypercriticism, impeachment, implication,
indictment, information, innuendo, insinuation, knock, lawsuit,
laying of charges, mark of Cain, nagging, niggle, niggling, nit,
nit-picking, obloquy, onus, overcriticalness, palaetiology,
personal remark, personality, pestering, pettifogging, pillorying,
placement, plaint, point champain, priggishness, prosecution,
quibble, quibbling, rap, reference to, reflection, reprimand,
reproach, reproachfulness, responsibility, saddling, slam, slur,
sly suggestion, smear, smirch, smudge, smutch, spot, stain, stigma,
stigmatism, stigmatization, stricture, suggestion, suit, swipe,
taint, taking exception, tarnish, taxing, trichoschistism,
true bill, uncomplimentary remark, unspoken accusation,
veiled accusation, whispering campaign

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英文字典中文字典相關資料:
  • Multiple Imputation by Chained Equations (MICE) Explained
    MICE is a multiple imputation method used to replace missing data values in a data set under certain assumptions about the data missingness mechanism (e g , the data are missing at random, the data are missing completely at random)
  • How should I determine what imputation method to use?
    If the missingness is MCAR or MAR then multiple imputation are helpful You can use something like MICE or predictive mean matching (side note: Frank implements this in his companion R package rms ) to use the information that is available -- including the outcome -- to impute the missing values
  • What is the difference between Imputation and Prediction?
    Typically imputation will relate to filling in attributes (predictors, features) rather than responses, while prediction is generally only about the response (Y) Even if imputation is being used to refer to filling in Y's the purpose is different; you're not using it for the primary purpose of getting a prediction for that Y
  • Dealing with MNAR data and imputation - Cross Validated
    The terminology might be getting in the way here If the data that you have explain the probability that other data are missing, then your data might be "missing at random" (MAR) in the technical sense, even if they are not "missing completely at random" (MCAR) In that case multiple imputation is a reasonable way to proceed
  • Imputation of missing data before or after centering and scaling?
    Imputation (better multiple imputation) is a way to fight this skewing But if you do imputation after scaling, you just preserve the bias introduced by the missingness mechanism Imputation is meant to fight this, and doing imputation after scaling just defeats this
  • Rubins rule from scratch for multiple imputations
    After multiple imputation of data sets (MI) and analyzing each of the imputed sets separately, Rubin's rules do have you take the mean over those imputations as the point estimate For inference, confidence intervals and so forth, you then determine the overall variance of the point estimate as a combination of within-imputation and between
  • Multiple imputation on single subscale item or subscale scores?
    Q2: Multiple imputation works by estimating missing values from values of the other variables in your data set (this being a non-technical explanation) So it does not matter if you recode your variables or not, if by recoding you mean changing the sign of the relation from one var to the other variables
  • Is multicollinearity problematic for imputation models?
    The way I read van Buuren's background on imputation, and this part specifically, is that for multiple imputation models the goal is to use as much information as you have in order to obtain the estimates required to complete any missing data This is grounded on the idea that multiple imputation is founded on the missing at random assumption
  • Creating a Pooled Data Set From Multiple Imputation Output in SPSS
    Pooling algorithms are given in the Multiple Imputation Pooling Algorithms chapter of the IBM SPSS Statistics Algorithms manual, which is available online (in the program, click Help>Documentation in PDF Format, select English or other desired language, then scroll down to the Manuals section and look for that title)
  • How to visualize models after multiple imputations by chained equations
    I'm starting to prefer visualizations of my regression models as opposed to tabular output (OR's, beta-coefficients, 95%CIs) However, I struggle to find a good way to do this when I am undertaking multiple imputation by chained equations (mice) The output of mice (in R) is usually one data frame containing m complete datasets after m imputations





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