英文字典中文字典Word104.com



中文字典辭典   英文字典 a   b   c   d   e   f   g   h   i   j   k   l   m   n   o   p   q   r   s   t   u   v   w   x   y   z   







請輸入英文單字,中文詞皆可:

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

請選擇你想看的字典辭典:
單詞字典翻譯
imputation查看 imputation 在Google字典中的解釋Google英翻中〔查看〕
imputation查看 imputation 在Yahoo字典中的解釋Yahoo英翻中〔查看〕





安裝中文字典英文字典查詢工具!


中文字典英文字典工具:
選擇顏色:
輸入中英文單字

































































英文字典中文字典相關資料:
  • 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
  • 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 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
  • 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
  • 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
  • Imputation of missing data before or after centering and scaling?
    I want to impute missing values of a dataset for machine learning (knn imputation) Is it better to scale and center the data before the imputation or afterwards? Since the scaling and centering m
  • 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?
  • Multiple imputation for outcome variables - Cross Validated
    Imputation itself adds uncertainty, for which reason multiple imputation is recommended, which basically explores, based on a range of seemingly "realistic" imputation values, how much uncertainty comes from the imputation (We should also have in mind that the real uncertainty is even larger, because the imputation model itself is uncertain )
  • when working with missing data, what percentage of data is considered . . .
    I am aware that there are assumptions that need to be held before proceeding with multiple imputation but in general what percentage of missing data would yo consider to be too much missing data? What other tools procedures would you recomend apart from multiple imputation?
  • mathematical statistics - Multiple imputation of outcome and time-to . . .
    My question is: can I impute (e g , using multiple imputation with the mice package in R) the outcome (incident disease) and the time to event for those excluded due to lack of follow-up data?





中文字典-英文字典  2005-2009

|中文姓名英譯,姓名翻譯 |简体中文英文字典