New application of the Clopper–Pearson method: a unified framework for extreme event proportion estimation

In this paper, we propose and evaluate an algorithm and a few variations to exclude aberrant landmarks before performing a maximum likelihood estimator in order to evaluate a mobile node position in a 1 hop indoor wireless network more accurately. We exclude data coming from landmarks based on a global likelihood to alleviate the effects of multipath propagation and replace this data with a constant bias.We compare the cases when one, two or three landmarks are excluded with the classical unbiased maximum likelihood estimator over three different testbeds. We then evaluate a heuristic approach that does not need to examine all possible subsets of landmarks but selects excluded landmarks based on a threshold on the bias. Results indicate that, depending on the testbed characteristics, one strategy or the other can lead to better results, but in most cases the performance is improved w.r.t. classical maximum likelihood estimation.

In recent years, person reidentification has received growing attention with the increasing popularity of intelligent video surveillance. This is because person reidentification is critical for human tracking with multiple cameras. Recently, keep it simple and straightforward (KISS) metric learning has been regarded as a top level algorithm for person reidentification. The covariance matrices of KISS are estimated by maximum likelihood (ML) estimation. It is known that discriminative learning based on the minimum classification error (MCE) is more reliable than classical ML estimation with the increasing of the number of training samples. When considering a small sample size problem, direct MCE KISS does not work well, because of the estimate error of small eigenvalues. Therefore, we further introduce the smoothing technique to improve the estimates of the small eigenvalues of a covariance matrix. Our new scheme is termed the minimum classification error-KISS (MCE-KISS). We conduct thorough validation experiments on the VIPeR and ETHZ datasets, which demonstrate the robustness and effectiveness of MCE-KISS for person reidentification.

In this paper, the idea of generalized maximum entropy estimation approach (Golan et al. 1996) is used to fit the general linear measurement error model. A Monte Carlo comparison is made with the classical maximum likelihood estimation (MLE) method. The results showed that, the GME is outperformed the MLE estimators in terms of mean squared error. A real data analysis is also presented.

In this work, we addressed parameter estimation and prediction in the high-dimensional sparse logistic regression model through both Monte Carlo simulations and application to real data. We applied two well-known penalized maximum likelihood (ML) methods (LASSO and aLASSO) for variable screening. There may exist overfitting from LASSO or underfitting from aLASSO, making ML estimators based on these models inefficient. Hence, after performing variable selection, we proposed post-selection improved estimation based on linear shrinkage, pretest, and James-Stein shrinkage strategies, which efficiently combine overfitted and underfitted ML estimators. Regardless of the correctness in the variable selection stage, the proposed estimators were shown to be more efficient than the classical ML estimators, which were severely affected by inappropriate variable selection.

The role of technology in advancing medical practice and improving patient outcomes is increasingly vital in today's healthcare landscape. In the past few decades, we have seen rapid advancements in medical technology, which have transformed how we diagnose, treat, and prevent diseases. Medical technology has brought about remarkable improvements in patient outcomes and quality of life, as well as reducing healthcare costs. One of the most significant advances in medical technology is the development of electronic health records (EHRs). EHRs allow healthcare providers to store patient information electronically, making it more accessible and easier to share between healthcare providers. With EHRs, healthcare providers can access patient records in real-time, enabling them to make informed decisions about patient care. EHRs also enable healthcare providers to track patient outcomes and improve the quality of care provided to patients. Medical imaging technology has also revolutionized medical practice. Medical imaging technology includes X-rays, CT scans, MRI, and PET scans. These technologies allow healthcare providers to visualize the inside of a patient's body, enabling them to diagnose and treat diseases more accurately and effectively. In addition, the development of 3D printing technology has allowed healthcare providers to create custom prosthetics and implants for patients, improving their quality of life. Telemedicine is another area where technology has advanced medical practice. Telemedicine allows healthcare providers to diagnose and treat patients remotely, reducing the need for in-person visits. This technology has been particularly useful in rural areas where patients have limited access to healthcare. Telemedicine also reduces healthcare costs by reducing the need for expensive in-person visits. In conclusion, technology has played a significant role in advancing medical practice and improving patient outcomes. From electronic health records to medical imaging technology and telemedicine, medical technology has transformed how we diagnose, treat, and prevent diseases. While there are still challenges to overcome, such as ensuring the security and privacy of patient information, it is clear that medical technology will continue to play a critical role in improving healthcare outcomes and reducing healthcare costs. As healthcare providers and technology companies work together to develop and implement new medical technologies, we can expect to see continued progress in the years to come.

When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix (Σ) of group-level varying coefficients are often degenerate. One can do better, even from a purely point estimation perspective, by using a prior distribution or penalty function. In this article, we use Bayes modal estimation to obtain positive definite covariance matrix estimates. We recommend a class of Wishart (not inverse-Wishart) priors for Σ with a default choice of hyperparameters, that is, the degrees of freedom are set equal to the number of varying coefficients plus 2, and the scale matrix is the identity matrix multiplied by a value that is large relative to the scale of the problem. This prior is equivalent to independent gamma priors for the eigenvalues of Σ with shape parameter 1.5 and rate parameter close to 0. It is also equivalent to independent gamma priors for the variances with the same hyperparameters multiplied by a function of the correlation coefficients. With this default prior, the posterior mode for Σ is always strictly positive definite. Furthermore, the resulting uncertainty for the fixed coefficients is less underestimated than under classical ML or restricted maximum likelihood estimation. We also suggest an extension of our method that can be used when stronger prior information is available for some of the variances or correlations.

As a result of asymmetry in practical problems, the Lognormal distribution is more suitable for data modeling in biological and economic fields than the normal distribution, while biases of maximum likelihood estimators are regular of the order O ( n − 1 ) , especially in small samples. It is of necessity to derive logical expressions for the biases of the first-order and nearly consistent estimators by bias correction techniques. Two methods are adopted in this article. One is the Cox-Snell method. The other is the resampling method known as parametric Bootstrap. They can improve maximum likelihood estimators performance and correct biases of the Lognormal distribution parameters. Through Monte Carlo simulations, we obtain average root mean squared error and bias, which are two important indexes to compare the effect of different methods. The numerical results reveal that for small and medium-sized samples, the performance of analytical bias correction estimation is superior than bootstrap estimation and classical maximum likelihood estimation. Finally, an example is given based on the actual data.

The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation study to assess the performance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general, all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contains outliers. The proposed estimators showed the best performance compared to other robust estimators.

Bayesian estimation of the network autocorrelation model

In this survey, we present a modified inverse moment estimation of parameters and its applications. We use a specific model to demonstrate its principle and how to apply this method in practice. The estimation of unknown parameters is considered. A necessary and sufficient condition for the existence and uniqueness of maximum-likelihood estimates of the parameters is obtained for the classical maximum likelihood estimation. Inverse moment and modified inverse moment estimators are proposed and their properties are studied. Monte Carlo simulations are conducted to compare the performances of these estimators. As far as the biases and mean squared errors are concerned, modified inverse moment estimator works the best in all cases considered for estimating the unknown parameters. Its performance is followed by inverse moment estimator and maximum likelihood estimator, especially for small sample sizes.

The estimation of the parameters of logit model is mostly performed with method of maximum likelihood. However, the classical maximum likelihood estimators are biased and inefficient in appearance of small samples. The jackknife maximum likelihood estimator improves the above problems but still includes serious disadvantages. In this article, the Bootstrap Maximum Likelihood Estimator is developed as an alternative advanced method for reducing the bias and correcting the troubles with inefficiency and nonnormality. The importance of the method is shown through its application on data of Greek mergers and acquisitions.

Bell regression model has become a very versatile model that replaced the conventional count data models and helps to resolve the problem of over-dispersion where the variance of data points surpasses the mean. Nevertheless, in practice, the classical maximum likelihood estimators (MLE) of the parameters of a model are frequently affected by multicollinearity among the explanatory variables, and they yield highly unstable estimates and inflated variances. To address these difficulties, estimation methods developed to estimate shrinkage, such as ridge or Liu estimators have been applied to the Bell regression model. In the subject review, new developments in estimators of shrinkage of Bell regression models are proportionate in discussing their theoretical background in knowledge, estimation process, and asymptotic characteristics. The results on Monte Carlo simulation studies always show that shrinkage estimators outweigh MLEs in that they minimize mean squared error and bias more than MLEs, especially in cases of multicollinearity. Both overall, estimation methods of shrinkage is a considerable improvement in Bell regression modeling that offers certainty and effectiveness of analysis of complicated counts information utilizing problematic distribution attributes.

Recent catastrophic failures of the rocket motor on a US missile caused concern in the US Navy that the missile might not be serviceable past its estimated service life of 20 years. This paper analyzes nearly 2000 firings of the motor under field conditions using classical maximum likelihood estimation (MLE) methods and Bayesian methods. MLE methods indicate that there could be less than a 1% chance that the motor will survive past 20 years of life and are not considered credible. Bayesian methods indicate that there is better than a 99% chance that the motors will survive past 20 years of life. The authors present reasons for preferring the Bayesian analysis and discuss testing schemes for more precise estimates. They comment on the lack of data to perform degradation analysis as a function of temperature cycling for the motor and make recommendations for future missile system data collection.

Binary Logistic Regression (BLR) is widely used for classification, but parameter estimation can be unstable due to reliance on numerical optimization, class separation, and high-dimensional data. This research aims to address these challenges by evaluating and comparing four distinct approaches to estimating BLR parameters: the classical Maximum Likelihood Estimation (MLE) based on the Newton-Raphson Method (NRM), regularized approaches via Gradient Descent (GD) with Lasso Regression (LR, L1) and Ridge Regression (RR, L2) penalties, and the metaheuristic Particle Swarm Optimization (PSO). To evaluate these methods, real-world clinical data from 1,511 breast cancer patients treated at Hiwa Hospital in Sulaymaniyah between 2019 and 2023 were utilized. The main objective was to evaluate each method’s predictive accuracy in identifying the need for chemotherapy, using evaluation metrics such as Area Under the Curve (AUC), Log-Loss, and Mean Squared Error (MSE). Using N-split Cross-Validation, advanced statistical tests were applied to confirm the significance and reliability of the observed differences, including the Friedman test, the Wilcoxon test, and the Holm correction procedure. Therefore, this study's primary goal is to determine the most reliable and effective technique for estimating BLR parameters. Consequently, statistical models in real-world applications are more accurate. According to the results, MLE and PSO performed noticeably better, obtaining the highest AUC values and the lowest levels of loss (Log-Loss and MSE). Their performance was consistent across all N-split values, with no significant differences between them in most statistical tests. LR performed mediocrely, falling short of MLE and PSO, especially in the AUC metric, though the differences were not always statistically significant. However, RR did not perform well. Its Log-Loss and MSE values were extremely high and markedly different from the other methods, even though its AUC was similar to the others. This illustrates the model's instability and poor probability estimation, likely caused by over-regularization.

Public support for reducing US reliance on fossil fuels: Investigating household willingness-to-pay for energy research and development