Multicollinearity and outliers remain two major challenges in linear regression modeling, often occurring simultaneously in practical applications and leading to instability, inflated variance, and unreliable inference. Although shrinkage estimators such as ridge and Liu estimators effectively address multicollinearity...
Accurate estimation of the population mean becomes challenging in the presence of outliers, especially when conventional estimators are implemented under simple random sampling. Ranked set sampling, known for its efficiency gains through judgment-based ranking, further suffers when extreme observations distort the esti...
We present a simple Gaussian approximation to the finite-sample distribution of the classical ridge regression estimator. Our approximation captures the fact that, in finite samples, the ridge regression estimator trades off bias and variance to reduce estimation and prediction error. Our approximation is based on nons...
It is obvious to say that an adequate estimation of the autocorrelation function is central in time series analysis. In this paper, we propose three new robust estimators based on ratios of observations, which offer strong resistance against outliers. While the first estimator, which is based on the median, is not effi...
Accurate estimation of population variance plays a vital role in survey sampling, especially when simple random sampling is used. In this work, we propose a new generalized statistical inference in order to estimate the population variance using auxiliary information. We can use the relationship between the study varia...
Robust inference for overdispersed count data is crucial in applications where outliers may substantially distort classical likelihood-based estimation of both the mean and dispersion. We develop robust estimation procedures for independent and identically distributed negative binomial data and provide practical guid...
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