964 research outputs found
Assumptions of IV Methods for Observational Epidemiology
Instrumental variable (IV) methods are becoming increasingly popular as they
seem to offer the only viable way to overcome the problem of unobserved
confounding in observational studies. However, some attention has to be paid to
the details, as not all such methods target the same causal parameters and some
rely on more restrictive parametric assumptions than others. We therefore
discuss and contrast the most common IV approaches with relevance to typical
applications in observational epidemiology. Further, we illustrate and compare
the asymptotic bias of these IV estimators when underlying assumptions are
violated in a numerical study. One of our conclusions is that all IV methods
encounter problems in the presence of effect modification by unobserved
confounders. Since this can never be ruled out for sure, we recommend that
practical applications of IV estimators be accompanied routinely by a
sensitivity analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS316 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
DETERMINANTS OF AGRICULTURE-RELATED LOAN DEFAULT: EVIDENCE FROM CHINA
This paper investigates agriculture-related loan default in 2002–2009 through a large data set from a leading Chinese state-owned bank. Using logit regression, we find the default rate on agriculture-related loans is significantly higher than that on non–agriculture-related loans. We find that base interest rates, loan maturity, the type of collateral, firm size, ownership structure, and managerial quality rating have a significant impact on agriculture-related loan default, but this also depends on how agriculture-related loans are defined. The results provide insight into the real impact of monetary policy on agriculture-related lending.This paper investigates agriculture-related loan default in 2002–2009 through a large data set from a leading Chinese state-owned bank. Using logit regression, we find the default rate on agriculture-related loans is significantly higher than that on non–agriculture-related loans. We find that base interest rates, loan maturity, the type of collateral, firm size, ownership structure, and managerial quality rating have a significant impact on agriculture-related loan default, but this also depends on how agriculture-related loans are defined. The results provide insight into the real impact of monetary policy on agriculture-related lending
A Hybrid Secure Scheme for Wireless Sensor Networks against Timing Attacks Using Continuous-Time Markov Chain and Queueing Model
Wireless sensor networks (WSNs) have recently gained popularity for a wide
spectrum of applications. Monitoring tasks can be performed in various
environments. This may be beneficial in many scenarios, but it certainly
exhibits new challenges in terms of security due to increased data
transmission over the wireless channel with potentially unknown threats. Among
possible security issues are timing attacks, which are not prevented by
traditional cryptographic security. Moreover, the limited energy and memory
resources prohibit the use of complex security mechanisms in such systems.
Therefore, balancing between security and the associated energy consumption
becomes a crucial challenge. This paper proposes a secure scheme for WSNs
while maintaining the requirement of the security-performance tradeoff. In
order to proceed to a quantitative treatment of this problem, a hybrid
continuous-time Markov chain (CTMC) and queueing model are put forward, and
the tradeoff analysis of the security and performance attributes is carried
out. By extending and transforming this model, the mean time to security
attributes failure is evaluated. Through tradeoff analysis, we show that our
scheme can enhance the security of WSNs, and the optimal rekeying rate of the
performance and security tradeoff can be obtained. View Full-Tex
A new efficient method for analysis of finite periodic structures
The electromagnetic modeling of practical finite periodic structures is a topic of growing interest. Due to the truncation of the infinite periodic structures, surface waves will be excited and localized near the discontinuous interfaces leading to the edge effect of finite structures. In this work, surface waves are numerically disentangled from the propagating Bloch waves contributions. Based on the universally exponential decay feature of the surface waves, a novel method is developed by connecting the solution to the large finite periodic structure with that to a relatively small one resulting in low complexity and memory consumption. The method numerically reconstructs propagating Bloch waves and surface waves according to the Bloch-Floquet theorem of periodic structures and translation invariant properties of semi-infinite periodic structures, respectively. Numerical examples are privided to validate the efficiency and accuracy of the newly developed method.postprin
Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm
Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS) of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO) algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods
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