Shift outliers in linear inference

نویسندگان

  • Donald R. Jensen
  • Donald E. Ramirez
چکیده

Shifts in responses typically are obscured to users, so that regression proceeds as if unshifted. At issue is the infusion of such shifts into the classical analysis. On projecting outliers into the “Regressor” and “Error” spaces of a model, our findings are that shifts in responses account for shifts in the OLS solutions and for inflated residuals. These in turn impact estimation, prediction, and hypothesis tests, all of vital interest to users, and all documented. Tools for identifying shifted responses are given. Case studies illustrate effects of such shifts, to include a reexamination of studies from the literature. AMS Subject Classification: 62J05 and 62J20

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Detection of Outliers and Influential Observations in Linear Ridge Measurement Error Models with Stochastic Linear Restrictions

The aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. Based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. In addition, we derive the corrected score test statistic for outliers detection ba...

متن کامل

Diagnostic Measures in Ridge Regression Model with AR(1) Errors under the Stochastic Linear Restrictions

Outliers and influential observations have important effects on the regression analysis. The goal of this paper is to extend the mean-shift model for detecting outliers in case of ridge regression model in the presence of stochastic linear restrictions when the error terms follow by an autoregressive AR(1) process. Furthermore, extensions of measures for diagnosing influential observations are ...

متن کامل

Prediction of toxicity of aliphatic carboxylic acids using adaptive neuro-fuzzy inference system

Toxicity of 38 aliphatic carboxylic acids was studied using non-linear quantitative structure-toxicityrelationship (QSTR) models. The adaptive neuro-fuzzy inference system (ANFIS) was used to construct thenonlinear QSTR models in all stages of study. Two ANFIS models were developed based upon differentsubsets of descriptors. The first one used log ow K and LUMO E as inputs and had good predicti...

متن کامل

Rfit: Rank-based Estimation for Linear Models

In the nineteen seventies, Jurečková and Jaeckel proposed rank estimation for linear models. Since that time, several authors have developed inference and diagnostic methods for these estimators. These rank-based estimators and their associated inference are highly efficient and are robust to outliers in response space. The methods include estimation of standard errors, tests of general linear ...

متن کامل

Identification of outliers types in multivariate time series using genetic algorithm

Multivariate time series data, often, modeled using vector autoregressive moving average (VARMA) model. But presence of outliers can violates the stationary assumption and may lead to wrong modeling, biased estimation of parameters and inaccurate prediction. Thus, detection of these points and how to deal properly with them, especially in relation to modeling and parameter estimation of VARMA m...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • J. Multivariate Analysis

دوره 136  شماره 

صفحات  -

تاریخ انتشار 2015