Nonparametric standard errors and confidence intervals

1981 ◽  
Vol 9 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Bradley Efron
Keyword(s):  
Confidence Intervals ◽  
Standard Errors

Uncertainty in protein–ligand binding constants: asymmetric confidence intervals versus standard errors

2021 ◽  
Author(s):  
Vaida Paketurytė ◽  
Vytautas Petrauskas ◽  
Asta Zubrienė ◽  
Olga Abian ◽  
Margarida Bastos ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Confidence Intervals ◽  
Binding Constants ◽  
Standard Errors

scholarly journals Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1944 ◽  
Author(s):  
Jessica Esteban ◽  
Ronald McRoberts ◽  
Alfredo Fernández-Landa ◽  
José Tomé ◽  
Erik Nӕsset
Keyword(s):  
Confidence Intervals ◽  
Random Forests ◽  
Remotely Sensed ◽  
Predictor Variables ◽  
Prediction Algorithm ◽  
Standard Errors ◽  
Original Structure ◽  
Remotely Sensed Data ◽  
Population Means ◽  
Model Based

Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables.

scholarly journals Inference in Regression Discontinuity Designs with a Discrete Running Variable

2018 ◽  
Vol 108 (8) ◽  
pp. 2277-2304 ◽  
Author(s):  
Michal Kolesár ◽  
Christoph Rothe
Keyword(s):  
Confidence Intervals ◽  
Conditional Expectation ◽  
Regression Discontinuity ◽  
Model Misspecification ◽  
Standard Errors ◽  
Moderate Number ◽  
Regression Discontinuity Designs ◽  
The Common ◽  
Present Simulation ◽  
Theoretical Results

We consider inference in regression discontinuity designs when the running variable only takes a moderate number of distinct values. In particular, we study the common practice of using confidence intervals (CIs) based on standard errors that are clustered by the running variable as a means to make inference robust to model misspecification (Lee and Card 2008). We derive theoretical results and present simulation and empirical evidence showing that these CIs do not guard against model misspecification, and that they have poor coverage properties. We therefore recommend against using these CIs in practice. We instead propose two alternative CIs with guaranteed coverage properties under easily interpretable restrictions on the conditional expectation function. (JEL C13, C51, J13, J31, J64, J65)

Using Bootstrapped Confidence Intervals for Improved Inferences with Seemingly Unrelated Regression Equations

1996 ◽  
Vol 12 (3) ◽  
pp. 569-580 ◽  
Author(s):  
Paul Rilstone ◽  
Michael Veall
Keyword(s):  
Monte Carlo ◽  
Regression Model ◽  
Confidence Intervals ◽  
Seemingly Unrelated Regression ◽  
Regression Coefficients ◽  
Standard Errors ◽  
Regression Equations ◽  
Unrelated Regression ◽  
Seemingly Unrelated Regression Model ◽  
Downward Bias

The usual standard errors for the regression coefficients in a seemingly unrelated regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper, Monte Carlo evidence is reported which indicates that bootstrapping can result in substantially better inferences when applied to t-ratios rather than to standard errors.

Sign in / Sign up

Export Citation Format

Share Document