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imputation    音标拼音: [,ɪmpjət'eʃən]
n. 归罪,负责,责难

归罪,负责,责难

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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  • How much missing data is too much? Multiple Imputation (MICE) R
    If the imputation method is poor (i e , it predicts missing values in a biased manner), then it doesn't matter if only 5% or 10% of your data are missing - it will still yield biased results (though, perhaps tolerably so) The more missing data you have, the more you are relying on your imputation algorithm to be valid
  • missing data - Test set imputation - Cross Validated
    As far as the second point - people developing predictive models rarely think how missing data occurs in application You need to have methods for missing values to render useful predictions - this is a "so called package deal" It seems hard to make a case that you can observe the future "test" set in batch and re-develop an imputation model
  • normalization - Should data be normalized before or after imputation of . . .
    9 I am working on a metabolomics data set of 81 samples x 407 variables with ~17% missing data I would like to compare a number of imputation methods to see which is best for my data Is there a general rule for the order of pre-treating a data set? Should I impute first and normalize after or normalize first?
  • How should I determine what imputation method to use?
    What imputation method should I use here and, more generally, how should I determine what imputation method to use for a given data set? I've referenced this answer but I'm not sure what to do from it
  • How do you choose the imputation technique? - Cross Validated
    I read the scikit-learn Imputation of Missing Values and Impute Missing Values Before Building an Estimator tutorials and a blog post on Stop Wasting Useful Information When Imputing 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
  • Multiple imputation and modelling using penalised splines
    I ran multiple imputation in R using mice Only one categorical variable had missingness and I specified the imputation model to imputate it using polyreg After imputation, I run the Cox model bel
  • Multiple Imputation by Chained Equations (MICE) Explained
    I have seen Multiple Imputation by Chained Equations (MICE) used as a missing data handling method Is anyone able to provide a simple explanation of how MICE works?
  • Imputation before or after splitting into train and test?
    The main analytic method will be Cox proportional hazards I plan to use multiple imputation I will also be splitting into a train and test set Should I split the data and then impute separately, or impute and then split? If it matters, I will be using PROC MI in SAS
  • Does this imputation with mice() make sense? - Cross Validated
    I am currently working on my first R project using medical data I wanted to use MICE imputation for a few variables, and I had a doubt If, for example, variable BMI had zero missing values, then





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