Poisson modelparametric robust regression approachregression parameterslegitimate likelihood functionsfinite second momentslog link functionidentity link function/ A0250 Probability theory, stochastic processes, and statistics B0240Z Other topics in statistics C1140Z Other topics in statistics...
Robust Poisson regression In robust Poisson regression, a quasi-likelihood (QL) model can be applied to fit the data with a binary outcome [14,15,16,17,18]. Quasi-likelihood was first introduced by Wedderburn (1974) as a function that has properties analog to those of log-likelihood functi...
Few researchers have extended the method to some generalized linear models such as the Poisson, the zero-inflated negative binomial and inverse gaussian regression models21,22,23. However, it is sensitive to outliers in the y-direction. In this study, we propose a robust version of the Stein ...
In the experimental phase, edge demands are generated according to Poisson distributions. Christiansen et al. (2009) focus on the undirected CARP with stochastic demands. In particular, the demands are random variables that follow a Poisson distribution. The problem is modelled as a two-stage ...
regress — Linear regression 3 Options £ £ Model noconstant; see [R] Estimation options. hascons indicates that a user-defined constant or its equivalent is specified among the independent vari- ables in indepvars. Some caution is recommended when specifying this option, as ...
Huber24 introduced the concept of M-estimation for regression, providing a more direct approach compared to M-estimation for location. In M-estimation, which involves minimizing a certain function of residuals, such as in case of location estimation, robustness of estimator hinges on the selection ...
Recently, brain-inspired computing models have shown great potential to outperform today’s deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown
Table 8 Regression model loss value on training and validation set for different number of Gaussian components in MDN Full size table Appendix 4: Progressive transfer learning processes of training the proposed deep learning framework See Tables9,10, and11. ...
We employ a MLE for the estimation of point source depth in both simulation and experimental settings. Our sensor model accounts for only Poisson noise. The parameters to be estimated include the point source location \((x,y,z)\), intensity \(A\), and background intensity \(B\). To co...
We tested for trend over time using linear regression with time as a continuous pre- dictor; data were square root transformed to satisfy assumptions of normality. To test for autocorrelation in the time-series data (and thus a lack of data independence) we: 1) examined the model residuals ...