Robust scalar-on-function partial quantile regression.

Compared with the conditional mean regression-based scalar-on-function regression model, the scalar-on-function quantile regression is robust to outliers in the response variable. However, it is susceptible to outliers in the functional predictor (called leverage points). This is because the influence function of the regression quantiles is bounded in the response variable but unbounded in the predictor space. The leverage points may alter the eigenstructure of the predictor matrix, leading to poor estimation and prediction results. This study proposes a robust procedure to estimate the model parameters in the scalar-on-function quantile regression method and produce reliable predictions in the presence of both outliers and leverage points. The proposed method is based on a functional partial quantile regression procedure. We propose a weighted partial quantile covariance to obtain functional partial quantile components of the scalar-on-function quantile regression model. After the decomposition, the model parameters are estimated via a weighted loss function, where the robustness is obtained by iteratively reweighting the partial quantile components. The estimation and prediction performance of the proposed method is evaluated by a series of Monte-Carlo experiments and an empirical data example. The results are compared favorably with several existing methods. The method is implemented in an R package robfpqr.

Factors Affecting Turkish Medical Students' Pursuit of a Career in Neurosurgery: A Single Center Survey Study.

BACKGROUND: Statistics show that over the past 2 decades, even in high-income countries, fewer and fewer students have listed neurosurgery as their top career option. Literature on medical students' pursuit of neurosurgical careers in middle- and low-income countries are scarce. The aim of this research, conducted in Turkey with a middle-income economy, was to contribute insights relevant to medical education and neurosurgery across the world. METHODS: A survey was conducted with a target sample of fourth-year (167 students), fifth-year (169 students), and sixth-year (140 students) medical students (476 in total) from the Medical School at Istanbul Medeniyet University in Turkey. The response rates of the fourth-, fifth-, and sixth-year students were 62% (104/167), 53% (90/169), and 50% (70/140), respectively (in total, 266, including 147 female and 119 male). RESULTS: In terms of the genuine intention, only 2.5% of men and 2.7% of women were committed to specializing in neurosurgery. This study further revealed that possible reasons for these students' low motivation to specialize in neurosurgery were their beliefs that in neurosurgery, the physical and psychological demands were high, and the night shifts were intense, meaning they would not have a social life or spare time for their hobbies; that morbidity/mortality were high; and that financial incentives were insufficient, especially in public institutions. CONCLUSION: Turkish medical students did not rank neurosurgery at the top of their career choices. Possible reasons for this are socioeconomic factors and the inadequate introduction of neurosurgery to medical students.

A simple mathematical tool to forecast COVID-19 cumulative case numbers.

OBJECTIVE: Mathematical models are known to help determine potential intervention strategies by providing an approximate idea of the transmission dynamics of infectious diseases. To develop proper responses, not only are more accurate disease spread models needed, but also those that are easy to use. MATERIALS AND METHODS: As of July 1, 2020, we selected the 20 countries with the highest numbers of COVID-19 cases in the world. Using the Verhulst-Pearl logistic function formula, we calculated estimates for the total number of cases for each country. We compared these estimates to the actual figures given by the WHO on the same dates. Finally, the formula was tested for longer-term reliability at t = 18 and t = 40 weeks. RESULTS: The Verhulst-Pearl logistic function formula estimated the actual numbers precisely, with only a 0.5% discrepancy on average for the first month. For all countries in the study and the world at large, the estimates for the 40th week were usually overestimated, although the estimates for some countries were still relatively close to the actual numbers in the forecasting long term. The estimated number for the world in general was about 8 times that actually observed for the long term. CONCLUSIONS: The Verhulst-Pearl equation has the advantage of being very straightforward and applicable in clinical use for predicting the demand on hospitals in the short term of 4-6 weeks, which is usually enough time to reschedule elective procedures and free beds for new waves of the pandemic patients.

Estimation in the partially nonlinear model by continuous optimization.

A useful model for data analysis is the partially nonlinear model where response variable is represented as the sum of a nonparametric and a parametric component. In this study, we propose a new procedure for estimating the parameters in the partially nonlinear models. Therefore, we consider penalized profile nonlinear least square problem where nonparametric components are expressed as a B-spline basis function, and then estimation problem is expressed in terms of conic quadratic programming which is a continuous optimization problem and solved interior point method. An application study is conducted to evaluate the performance of the proposed method by considering some well-known performance measures. The results are compared against parametric nonlinear model.