Instrumental variable estimation with heteroskedasticity and many instruments

It is common practice in econometrics to correct for heteroskedasticity.This paper corrects instrumental variables estimators with many instruments for heteroskedasticity.We give heteroskedasticity robust versions of the limited information maximum likelihood (LIML) and Fuller (1977, FULL) estimators; as well as heteroskedasticity consistent standard errors thereof. The estimators are based on removing the own observation terms in the numerator of the LIML variance ratio. We derive asymptotic properties of the estimators under many and many weak instruments setups. Based on a series of Monte Carlo experiments, we find that the estimators perform as well as LIML or FULL under homoskedasticity, and have much lower bias and dispersion under heteroskedasticity, in nearly all cases considered.

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

Outcomes in Human Immunodeficiency Virus Infected Recipients of Heart and Lung Transplants

Background: With the advent of combined antiretroviral therapy (cART), growing evidence has shown human immunodeficiency virus (HIV) may no longer be an absolute contraindication for solid organ transplantation. This study compares outcomes of heart transplantations between HIV‐positive and HIV‐negative recipients using SRTR transplant registry data. Methods: Patient survival, overall graft survival and death‐censored graft survival were compared between HIV‐positive and HIV‐negative recipients. Multivariate Cox regression and Cox regression with a disease risk score (DRS) methodology were used to estimate the adjusted hazard ratios among heart transplant recipients (HTRs). Results: In total, 35 HTRs with HIV+ status were identified. No significant differences were found in patient survival (88% vs 77%; P = 0.1493), overall graft survival (85% vs 76%; P = 0.2758), and death‐censored graft survival (91% vs 91%; P = 0.9871) between HIV‐positive and HIV‐negative HTRs in 5‐year follow‐up. No significant differences were found after adjusting for confounders. Conclusions: This study supports the use of heart transplant procedures in selected HIV‐positive patients. This study suggests that HIV‐positive status is not a contraindication for life‐saving heart transplant as there were no differences in graft, patient survival

A Comment on "A direct approach for determining the switch soints in the Karnik-Mendel algorithm"

This letter is a supplement to the previous paper “A Direct Approach for Determining the Switch Points in the Karnik-Mendel Algorithm”. In the previous paper, the enhanced iterative algorithm with stop condition (EIASC) was shown to be the most inefficient in R. Such outcome is apparently different from the results in another paper in which EIASC was illustrated to be the most efficient in Matlab. An investigation has been made into this apparent inconsistency and it can be confirmed that both the results in R and Matlab are valid for the EIASC algorithm. The main reason for such phenomenon is the efficiency difference of loop operations in R and Matlab. It should be noted that the efficiency of an algorithm is closely related to its implementation in practice. In this letter, we update the comparisons of the three algorithms in the previous paper based on optimised implementations under five programming languages (Matlab, R, Python, C and Java). From this, we conclude that results in one programming language cannot be simply extended to all languages