Topic
Partial residual plot
About: Partial residual plot is a research topic. Over the lifetime, 171 publications have been published within this topic receiving 4968 citations.
Papers published on a yearly basis
Papers
TL;DR: In this article, a new class of graphical and numerical methods for checking the adequacy of the Cox regression model is presented, derived from cumulative sums of martingale-based residuals over follow-up time and covariate values.
Abstract: SUMMARY This paper presents a new class of graphical and numerical methods for checking the adequacy of the Cox regression model The procedures are derived from cumulative sums of martingale-based residuals over follow-up time and/or covariate values The distributions of these stochastic processes under the assumed model can be approximated by zero-mean Gaussian processes Each observed process can then be compared, both visually and analytically, with a number of simulated realizations from the approximate null distribution These comparisons enable the data analyst to assess objectively how unusual the observed residual patterns are Special attention is given to checking the functional form of a covariate, the form of the link function, and the validity of the proportional hazards assumption An omnibus test, consistent against any model misspecification, is also studied The proposed techniques are illustrated with two real data sets
1,475 citations
TL;DR: Passing and Bablok regression analysis is a statistical procedure that allows valuable estimation of analytical methods agreement and possible systematic bias between them and it is robust, non-parametric, non sensitive to distribution of errors and data outliers.
Abstract: The comparison of methods experiment is important part in process of analytical methods and instruments validation. Passing and Bablok regression analysis is a statistical procedure that allows valuable estimation of analytical methods agreement and possible systematic bias between them. It is robust, non-parametric, non sensitive to distribution of errors and data outliers. Assumptions for proper application of Passing and Bablok regression are continuously distributed data and linear relationship between data measured by two analytical methods. Results are presented with scatter diagram and regression line, and regression equation where intercept represents constant and slope proportional measurement error. Confidence intervals of 95% of intercept and slope explain if their value differ from value zero (intercept) and value one (slope) only by chance, allowing conclusion of method agreement and correction action if necessary. Residual plot revealed outliers and identify possible non-linearity. Furthermore, cumulative sum linearity test is performed to investigate possible significant deviation from linearity between two sets of data. Non linear samples are not suitable for concluding on method agreement.
284 citations
TL;DR: In this paper, the authors define partial residuals in multiple linear regression as the dependent variable vector corrected for all independent variables except the ith variable, and show the extent and direction of linearity, while displaying deviations from linearity.
Abstract: This paper defines partial residuals in multiple linear regression. The ith partial residual vector can be thought of as the dependent variable vector corrected for all independent variables except the ith variable. A plot of the ith partial residuals vs values of the ith variable is proposed as a replacement for the usual plot displaying ordinary residuals vs the ith independent variable. This partial residual plot shows the extent and direction of linearity, while displaying deviations from linearity, such as outliers, inhomogeneity of variance, and curvilinear relationships. Some alternative definitions of partial residuals are described.
240 citations
TL;DR: In this article, a simple position follow-up system is considered for convenience although the method is applicable in the same form for higher order systems or those in which only empirical frequency data is known.
Abstract: The purpose of this paper is to demonstrate some graphical methods for finding the transient response of a control system. A simple position follow-up system is considered for convenience although the method is applicable in the same form for higher order systems or those in which only empirical frequency data is known. The basic procedure is to find the roots of the differential equation which correspond to the exponential transient terms which dominate the response. Doctor Profos1 of Switzerland points out that the plot of the function which describes the system from error to output is a function of a complex variable of which frequency is the imaginary part and damping is the real part. The Nyquist plot is thus one line of a conformal map with the root of the equation being the value of the variable which makes the function equal to -1. Any line of plot can be calculated for systems with known functions with essentially the same ease as the Nyquist plot by use of some graphical tricks. The amplitude of any transient term is determined from the plot once the root is known by use of a theorem of operational calculus. The development possibilities of the subject seem to be very great as suggested by several topics not yet investigated.
204 citations
TL;DR: The results indicate that VCPA is a good variable selection strategy when compared with four high performing variable selection methods: genetic algorithm-partial least squares (GA-PLS), Monte Carlo uninformative variable elimination by PLS, competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV).
180 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 4 |
| 2020 | 1 |
| 2019 | 3 |
| 2018 | 4 |
| 2017 | 3 |
| 2016 | 9 |