Residuals versus fitted values for the regression
Neoclassical versus frontier production models? Testing for the skewness of regression residuals
... We performed the proposed tests to the classic data sets of the U.S. electricity firms from years 1955 and 1970, using a variety of different regression methods to estimate the production technology. Our analysis ...
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Residuals and regression diagnostics: focusing on logistic regression
... He is experienced in data management and statistical analysis by using R and STATA, big data exploration, systematic review and meta-analysis. Zhongheng Zhang, MMed. Abstract: Up to now I have introduced most steps in ...
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Parametric modeling of dependence of bivariate quantile regression residuals' signs
... 38 CHAPTER 2. MULTIPLE RESPONSES To better illustrate and understand the importance of the study of multivariate dependencies in multiple response problems we will consider the following example. We refer for simplicity ...
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Empirical Investigation of Randomized Quantile Residuals for Diagnosis of Non-Normal Regression Models
... quantile residuals and theoretically justified the normality of this type of residual and compared its performance with the traditional ones, Pearson and deviance residuals, through a set of simulation ...
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Modeling the Residuals of Financial Time Series with Missing Values for Risk Measures Using R
... the residuals in order to compute some risk ...average, regression imputation and repetition were used in computing the missing ...missing values. The distribution of the residuals was found ...
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Surrogate residuals
... the fitted model does not include it, we can detect this misspecification using the surrogate ...S versus x 1 for the treatment group, and compare it to the plot of R S versus x 1 for the controls—or ...
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Regression analysis with compositional data containing zero values
... The number 2 is there because in the case of D = 2 we end up with the log-likelihood of the binary logistic regression. The Kullback-Leibler divergence (7) takes into account the divergence or the distance of each ...
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Approximation of Bayesian predictive p-values with regression ABC
... the fitted regression model in the subsequent analysis in order to avoid further costly data generation ...same regression adjustment techniques used for posterior approximation can also be used to ...
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On pseudo-values for regression analysis in competing risks models
... To investigate time-dependency the pseudo-values for each patient were calculated every 100 days. The model was fitted at each time point separately. Solutions for the covariates Stem type and gender are ...
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Computer Simulates the Effect of Internal Restriction on Residuals in Linear Regression Model with First order Autoregressive Procedures
... the residuals. At the same time, it allows the distribution of the residuals to represent more properties of the ...the residuals can be an estimator of the errors if the sample size effect is ...
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Career Concerns versus Shared Values: An Empirical Investigation
... “my job requires that I work very hard”. Individuals who agree strongly with this statement work on average 50% more overtime hours than those who do not. The 2011 wave of WERS also includes a question to employees about ...
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A Nu test for serial correlation of regression model residuals
... The exact test based on the Durbin-Watson statistic (whose distribution.. depends on the design matrix) generally performed somewhat better than the NU test in the. cases under simulatio[r] ...
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Screening outliers in process control regression data with uniform residuals, II
... As for univariate data, the exact distribution theory of these statistics allows a precise control of the number of cases incorrectly rejected when the data are generated by a regression[r] ...
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Accounting Values versus Market Values and Earnings Management in Banks
... that the CEO still has an incentive to hold INV and liquidate it incrementally based on the RP function. The value of this function is somewhat lower for each ratio of b 1 /b 2 than for the case depicted in Figure 2, ...
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GMM estimation of the autoregressive parameter in a spatial autoregressive error model using regression residuals
... This paper suggests an improved GMM estimator for the autoregres- sive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are different from ...
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Imputation of missing values using quantile regression
... Table 2.1-2.4 present the Monte Carlo relative biases and variances of the seven estima- tors for four models under two missing. To enable easy comparisons between the biases of five missing imputation methods, we plot ...
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Comments on the weighted regression approach to missing values
... The set of incomplete observations of a particular type — that is with the same variables missing — do not permit estimation of all the coefficients.. What the sums of squares and cros[r] ...
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Robust Regression Estimators When There are Tied Values
... tied values among the dependent variable, several robust estimators can result in poor control over the Type I error probability and relatively low power, so they should be used with ...tied values does not ...
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Biodiversity versus nature : values in conflict
... 23 However, it could be argued that the identification of inirinsic value is dependent on sensations of inherent value as it is unlikely that some aspect of nature would be considere[r] ...
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SUBJECTIVE VALUES VERSUS OBJECTIVE SCIENCE
... The purpose of this paper is to expose the bias in much of our social science research. The values and ethics of authors often influence their research procedures and results. Thus, the public gets a distorted (or ...
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