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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
  • 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
  • How do you choose the imputation technique? - Cross Validated
    scikit-learn provides three imputation strategies: SimpleImputer(), IterativeImputer(), and KNNImputer() I'd like to know how to decide which imputer to use I get that SimpleImputer() is best for cases where there are only a small number of missing observations, and where missingness in one feature is not affected by other features
  • elastic net - Multiple Imputation for Predictors Only, Excluding . . .
    While the majority of observations are complete cases, there are some observations with missing values in either some of the predictors, the outcome, or both My current approach is to remove all observations with missing outcome data from the analysis, and use mice (in R) to perform multiple imputation for missing values of the predictors
  • Imputation: why impute missing test set values from train set?
    I've consulted other posts here regarding imputation and the recommendation seems to be to not impute on unpartitioned data Data should be partitioned and missing values in both train and test set should be imputed from the mean (or whatever imputation method is chosen) computed out of training set
  • 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
  • missing data - Hot deck imputation, it preserves the distribution of . . .
    Hot-deck imputation of missing values is one of the simplest single-imputation methods The method - which is intuitively obvious - is that a case with missing value receives valid value from a case randomly chosen from those cases which are maximally similar to the missing one, based on some background variables specified by the user (these variables are also called "deck variables")





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