scholarly journals Asymptotics of nonparametric L-1 regression models with dependent data

Bernoulli ◽  
2014 ◽  
Vol 20 (3) ◽  
pp. 1532-1559
Author(s):  
Zhibiao Zhao ◽  
Ying Wei ◽  
Dennis K.J. Lin
Keyword(s):  
Regression Models ◽  
Dependent Data

Design effects for binary regression models fitted to dependent data

1993 ◽  
Vol 12 (13) ◽  
pp. 1259-1268 ◽  
Author(s):  
John M. Neuhaus ◽  
Mark R. Segal
Keyword(s):  
Regression Models ◽  
Dependent Data ◽  
Binary Regression ◽  
Design Effects

scholarly journals Prediction of Consensus Tropical Cyclone Track Forecast Error

2007 ◽  
Vol 135 (5) ◽  
pp. 1985-1993 ◽  
Author(s):  
James S. Goerss
Keyword(s):  
Tropical Cyclone ◽  
Regression Models ◽  
Average Distance ◽  
Forecast Error ◽  
Dependent Data ◽  
Track Forecast ◽  
Consensus Model ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
National Hurricane Center

Abstract The extent to which the tropical cyclone (TC) track forecast error of a consensus model (CONU) routinely used by the forecasters at the National Hurricane Center can be predicted is determined. A number of predictors of consensus forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic basin in 2001–03. Leading predictors were found to be consensus model spread, defined to be the average distance of the member forecasts from the consensus forecast, and initial and forecast TC intensity. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the CONU TC track forecast error. The percent variance of CONU TC track forecast error that could be explained by these regression models ranged from just over 15% at 48 h to nearly 50% at 120 h. Using the regression models, predicted radii were determined and were used to draw circular areas around the CONU forecasts that contained the verifying TC position 73%–76% of the time. Based on the size of these circular areas, a forecaster can determine the confidence that can be placed upon the CONU forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.

Inference for Odds Ratio Regression Models with Sparse Dependent Data

Biometrics ◽  
1998 ◽  
Vol 54 (1) ◽  
pp. 136 ◽  
Author(s):  
John J. Hanfelt ◽  
Kung-Yee Liang
Keyword(s):  
Odds Ratio ◽  
Regression Models ◽  
Dependent Data

scholarly journals Heterogeneous regression models for clusters of spatial dependent data

2020 ◽  
Vol 15 (4) ◽  
pp. 459-475 ◽  
Author(s):  
Zhihua Ma ◽  
Yishu Xue ◽  
Guanyu Hu
Keyword(s):  
Regression Models ◽  
Dependent Data

scholarly journals ENDOGENEITY IN SEMIPARAMETRIC THRESHOLD REGRESSION

2021 ◽  
pp. 1-34
Author(s):  
Andros Kourtellos ◽  
Thanasis Stengos ◽  
Yiguo Sun
Keyword(s):  
Monte Carlo ◽  
Approximation Method ◽  
Regression Models ◽  
Control Function ◽  
Dependent Data ◽  
Threshold Effects ◽  
Threshold Regression ◽  
Control Function Approach ◽  
Weakly Dependent Data ◽  
Weakly Dependent

This paper estimates threshold regression models with an endogenous threshold variable using a nonparametric control function approach. Assuming diminishing threshold effects, we derive the consistency and limiting distribution of our proposed estimator constructed from the series approximation method for weakly dependent data. In addition, we propose a test for the endogeneity of the threshold variable, which is valid regardless of whether the threshold effects exist. We assess the performance of our methods using Monte Carlo simulations.

scholarly journals Weighted scores method for regression models with dependent data

Biostatistics ◽  
2011 ◽  
Vol 12 (4) ◽  
pp. 653-665 ◽  
Author(s):  
A. K. Nikoloulopoulos ◽  
H. Joe ◽  
N. R. Chaganty
Keyword(s):  
Regression Models ◽  
Dependent Data
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