当变量A=110时,变量B=&quot什么意思;中国&quot什么意思;;当变量A=210时,变量B=“亚洲”,用C语言怎么写。

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a propensity score adjustment method for regression models with nonignorable missing covariates:一个具有不可忽略缺失的协变量回归模型校正倾向评分方法.pdf22页
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a propensity score adjustment method for regression models with nonignorable missing covariates:一个具有不可忽略缺失的协变量回归模型校正倾向评分方法
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Computational Statistics and Data Analysis 94
Contents lists available at ScienceDirect Computational Statistics and Data Analysis journal homepage: /locate/csda
A propensity score adjustment method for regression models
with nonignorable missing covariates
Depeng Jiang a,?, Puying Zhao a , Niansheng Tang b
a Department of Community Health Sciences, University of Manitoba, Canada
b Department of Statistics, Yunnan University, PR China
o a b s t r a c t
Article history: In a linear regression model with nonignorable missing covariates, non-normal errors or
Received 7 July 2014 outliers can lead to badly biased and misleading results with standard parameter estima-
Received in revised form 21 July 2015 tion methods built on either least squares- or likelihood-based methods. A propensity score
Accepted 21 July 2015 method with a robust and efficient regression procedure called composite quantile regres-
Available online 14 August 2015 sion for parameter estimation of the linear regression model with nonignorable missing covariates is proposed. Semiparametric estimation of the propensity score is based on the
Keywords: exponentially tilted likelihood approach. Asymptotic properties of the proposed estimators
Exponentially tilted likelihood
Composite quantile regression are systematically investigated. The proposed method is resistant to heavy-tailed errors or
Not missing at random outliers in the response. Simulation studies and real data applications are used to illustrate
Propensity score its potential impacts and benefits compared with conventional methods. ? 2015 Elsevier B.V. All rights reserved.
Introduction Missing values are commonly encountered in statistical applications. Ignoring the missing data will undermine study
efficiency, and may lead to misleading conclusions. For a complete review on missing data inference, see Little and Rubin 2002
正在加载中,请稍后...阅读材料:若a,b都是非负实数,则a+b≥
.当且仅当a=b时,“=”成立.证明:∵(
) 2 ≥0,∴a﹣
+b≥0.∴a+b≥
.当且仅当a=b时,“=”成立.举例应用:已知x>0,求函数y=2x+
的最小值.解:y=2x+
=4.当且仅当2x=
,即x=1时,“=”成立.当x=1时,函数有最小值,y 最小 =4.问题解决:汽车的经济时速是指汽车最省油的行驶速度.某种汽车在每小时70~110公里之间行驶时(含70公里和110公里),每公里耗油(
)升.若该汽车以每小时x公里的速度匀速行驶,1小时的耗油量为y升.(1)求y关于x的函数关系式(写出自变量x的取值范围);(2)求该汽车的经济时速及经济时速的百公里耗油量(结果保留小数点后一位).
牛阿乾ohDU10
(70≤x≤110)(2)90千米/小时&&& 11.1升
(1)∵汽车在每小时70~110公里之间行驶时(含70公里和110公里),每公里耗油(
)升.∴y=x×(
(70≤x≤110);(2)根据材料得:当
时有最小值,解得:x=90∴该汽车的经济时速为90千米/小时;当x=90时百公里耗油量为100×(
)≈11.1升.(1)根据1小时的耗油量=每公里的耗油量×行驶的速度列出函数关系式即可;(2)经济时速就是耗油量最小时的行驶速度,进而求出经济时速的百公里耗油量.
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你可能喜欢1、当逻辑函数有n个变量时,共有( D )个变量取值组合.A、n B、2n C、n2 D、2n如题 答案给的2n 不理解
琳子爷AGRZ
1、当逻辑函数有n个变量时,共有( D )个变量取值组合.A、n B、2n C、n2 D、2n当逻辑函数有n个变量时,共有( 2^n )个变量取值组合例,当逻辑函数有2个变量时,有00,01,10,11,共2^2=4个变量取值组合;当逻辑函数有3变量时,有000,001,010,011,100,101,110,111,共2^3=8个变量取值组合;
可能是我输入错误 B选项是2^n D是2n
答案给的2n 不懂是什么意思 还是答案错了?
2^n是绝对正确的
我感觉也是 但是答案给的就是2n 也不是非常确定 在网上找的题
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