Censored nonlinear regression models are often based on Gaussian errors for analytical convenience. However, real data—such as income levels, HIV viral loads, or agricultural yields—often exhibit asymmetry, heavy tails, outliers, and censored observations, making classical Gaussian models inadequate. In this study, we extend censored nonlinear regression (CNLR) using the two–piece scale mixture of normal (TP–SMN), a flexible distribution that accommodates both asymmetry and excess kurtosis. We develop an EM–type algorithm that allows exact maximum likelihood estimation without stochastic EM methods. The method’s effectiveness is demonstrated on simulated and real datasets, including Ultrasonic and Oil data. The results show that the proposed TP–SMN–CNLR models are robust against asymmetric behavior, outliers and censoring, providing accurate and reliable inference even under challenging data conditions.
We develop a class of semiparametric zero-altered models (ZAMs) for count data, estimated through the generalized method of moments (GMM). The proposed framework extends the utility of ZAMs by allowing for both under- and over-dispersion while preserving support on the entire set of natural numbers. This offers an alte...
S. Ghosh, Timothy Boakye, Kimberly F. Sellers· 0 citations
Skewed distributions often contain shape parameters that determine the direction and magnitude of asymmetry. In other cases, skewness arises naturally from the form of the distribution. Ignoring skewness when modeling with symmetric distributions may yield biased or misleading inferences. Bayesian regularized quantile...
F. N. Abdulahad, M. K. Majahar Ali, Alaa Adnan· Sains Malaysiana· 0 citations
In this paper, we propose a new kernel M-estimator of the regression function using nonparametric methods for twice-censored data. The almost sure (a.s.) convergence with rate over a compact set of the estimator is established under appropriate conditions using an unbounded score function. As a by-product, we build a N...
Wafaa Djelladj, Hassiba Benseradj, Z. Guessoum· Pakistan Journal of Statisti...· 0 citations
Estimating regression models with nonlinear predictor transformations is challenging when data are missing, because nonlinearity typically renders the conditional distribution of the missing values intractable. Previous methods require specific nonlinear forms, such as polynomials or interactions, or rely on approximat...
The two-parameter exponential distribution is widely used in reliability analysis and quality control, particularly for modeling failure times of products or processes. However, in practical scenarios, data collection is often incomplete due to time and resource limitations, necessitating the use of censoring technique...
Mohammad Vali Ahmadi, M. Doostparast· International Journal of Rel...· 0 citations
Bounded response variables, such as rates, proportions, and indices, frequently arise in the applied sciences and require regression models that respect the unit interval support, rather than relying on Gaussian regression. In this work, we propose a new regression model for continuous bounded data based on the unit Gu...
O. Alqasem, A. Gemeay, D. Alnagar et al.· Boundary Value Problems· 0 citations
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