heteroscedastic errors
The performance of leverage based near neighbour-robust weight least squares in multiple linear regression in the presence of heteroscedastic errors and outlier
... of heteroscedastic errors and outliers in multiple linear ...contain heteroscedastic errors and different percentages of outliers with different sample ...standard errors compared to ...
7
The TSR-MM Based on Robust Location and Scales Measures in Dual Response Optimization in the Presence of Outliers and Heteroscedastic Errors
... The dual response surface optimization approach is commonly used in an industrial process to simultaneously optimize the process sample mean and the process sample standard deviation functions. The shortcoming of this ...
10
Spatially Adaptive Bayesian P-Splines with Heteroscedastic Errors
... Figure 8-(c) shows that the standard deviation of the error process increases nonlinearly with the covariate and the variability around the standard deviation increases as well. As we mentioned, the object of inference ...
36
Outlier Detections and Robust Estimation Methods for Nonlinear Regression Model Having Autocorrelated and Heteroscedastic Errors
... AND HETEROSCEDASTIC ERRORS” in accordance with Universiti Putra Malaysia (Higher Degree) Act 1980 and Universiti Putra Malaysia (Higher Degree) Regulations ...
15
Statistical inference of partially linear regression models with heteroscedastic errors
... In this paper we have developed a procedure to conduct statistical inference on a partially linear regression model with independent but heteroscedastic errors. The large sample test of heteroscedasticity ...
19
The performance of robust estimator on linear regression model having both continuous and categorical variables with heteroscedastic errors
... The unweighted estimates are not efficient in the situation of the heteroscedastic errors. The empirical study shows that the weighting scheme has improved the accuracy of the three estimates. It appears ...
24
On the Conditional and Unconditional Type I Error Rates and Power of Tests in Linear Models with Heteroscedastic Errors
... standard errors [i.e., square root of the diag(HC4)] will differ from those found using OLS estimation. Based on simulation research, HC4 performs the best compared to existing HCCMs in terms of control over Type ...
27
Empirical likelihood for heteroscedastic partially linear models
... in many applications. Zhu and Xue [ 21 ] investigated likelihood confidence regions in a partially linear single-index model and treated the partially linear model as a special case of that model. They considered ...
10
Improved Inference of Heteroscedastic Fixed Effects Models
... with heteroscedastic errors but it provides erroneous inference for the data sets including high leverage ...improve heteroscedastic consistent covariance matrix estimator (HCCME) for panel dataset ...
20
The adaptive Lasso in high dimensional sparse heteroscedastic models
... dent identical distributed errors. To our best knowledge there has been no attempt to investigate bridge estimators and the adaptive Lasso in high dimensional linear models with heteroscedastic ...
30
An Approximate Hotelling T2 Test for Heteroscedastic One Way MANOVA
... (AHT) test. The AHT test can be regarded as a natural extension of [8]’s test and [5]’s MNV test from for the multivariate two-sample BF problem to for the GLHT problem in heteroscedastic one-way MANOVA. In view ...
11
Robust maximum likelihood training of heteroscedastic probabilistic neural networks
... two heteroscedastic PNNs had better layouts of the pattern ...The heteroscedastic PNN, how- ever, failed when the number of pattern units was increased to six or ...the heteroscedastic PNN to ...
9
A New Pearson-Type QMLE for Conditionally Heteroscedastic Models
... and rank-based estimators can only estimate the heteroscedastic parameters α i and β j under the same re-parameterized GARCH(p, q) model. Morevoer, it is worth noting that when ε t has an infinite fourth moment, ...
30
Linear Dimension Reduction for Multiple Heteroscedastic Multivariate Normal Populations
... Using the Bayes classification procedure in which we assume equal costs of misclassification and that all class parameters are known, we determine the reduced dimension q < p that is the[r] ...
23
Robust Diagnostics and Estimation in Heteroscedastic Regression Model in the Presence of Outliers
... in Heteroscedastic Regression Model in the Presence of Outliers” in accordance with Universities and University Colleges Act 1971 and the Constitution of the Universiti Putra Malaysia [ ...
16
Contributions to Conditional Heteroscedastic Models: M-Estimation and Other Methods.
... This estim ator provides good results for the estim ation of the param eters of ARCH models and also produces better volatility forecasts th a n the QMLE in almo[r] ...
261
On the existence and uniqueness of estimates in robust and heteroscedastic regression models
... Lange, Little, and Taylor (1989) report a study of maximum likelihood esti m ation for regression models with assumed t errors, and note its equivalence to redescending M-estimation. However their approach to the ...
91
Adaptive Estimation of Heteroscedastic Money Demand Model of Pakistan
... standard errors of the estimated coefficients, standard error of regression, Akaike Information Criteria (AIC) value, and the Durban-Watson statistic for ...
7
Estimating the Error Distribution in the Multivariate Heteroscedastic Time Series Models
... The values for the copula parameter θ were chosen so that the corresponding Kendall’s tau takes the values 0.2, 0.5 and 0.8, except for the Ali-Mikhail-Haq family because its tau value cannot be as high as 0.5. ...
28
Underwater Noise Modeling and Direction-Finding Based on Heteroscedastic Time Series
... In this section, we demonstrate the performance of the pro- posed approach for modeling of the additive noise in pas- sive sonar with two major experiments. In the first exper- iment, we use the recorded noise with one ...
10