Techniques for the Fast Simulation of Models of Highly dependable Systems

With the ever-increasing complexity and requirements of highly dependable systems, their evaluation during design and operation is becoming more crucial. Realistic models of such systems are often not amenable to analysis using conventional analytic or numerical methods. Therefore, analysts and designers turn to simulation to evaluate these models. However, accurate estimation of dependability measures of these models requires that the simulation frequently observes system failures, which are rare events in highly dependable systems. This renders ordinary Simulation impractical for evaluating such systems. To overcome this problem, simulation techniques based on importance sampling have been developed, and are very effective in certain settings. When importance sampling works well, simulation run lengths can be reduced by several orders of magnitude when estimating transient as well as steady-state dependability measures. This paper reviews some of the importance-sampling techniques that have been developed in recent years to estimate dependability measures efficiently in Markov and nonMarkov models of highly dependable system

Robust and Efficient Uncertainty Quantification and Validation of RFIC Isolation

Modern communication and identification products impose demanding constraints on reliability of components. Due to this statistical constraints more and more enter optimization formulations of electronic products. Yield constraints often require efficient sampling techniques to obtain uncertainty quantification also at the tails of the distributions. These sampling techniques should outperform standard Monte Carlo techniques, since these latter ones are normally not efficient enough to deal with tail probabilities. One such a technique, Importance Sampling, has successfully been applied to optimize Static Random Access Memories (SRAMs) while guaranteeing very small failure probabilities, even going beyond 6-sigma variations of parameters involved. Apart from this, emerging uncertainty quantifications techniques offer expansions of the solution that serve as a response surface facility when doing statistics and optimization. To efficiently derive the coefficients in the expansions one either has to solve a large number of problems or a huge combined problem. Here parameterized Model Order Reduction (MOR) techniques can be used to reduce the work load. To also reduce the amount of parameters we identify those that only affect the variance in a minor way. These parameters can simply be set to a fixed value. The remaining parameters can be viewed as dominant. Preservation of the variation also allows to make statements about the approximation accuracy obtained by the parameter-reduced problem. This is illustrated on an RLC circuit. Additionally, the MOR technique used should not affect the variance significantly. Finally we consider a methodology for reliable RFIC isolation using floor-plan modeling and isolation grounding. Simulations show good comparison with measurements

General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv

The R package frailtySurv for simulating and fitting semi-parametric shared frailty models is introduced. Package frailtySurv implements semi-parametric consistent estimators for a variety of frailty distributions, including gamma, log-normal, inverse Gaussian and power variance function, and provides consistent estimators of the standard errors of the parameters' estimators. The parameters' estimators are asymptotically normally distributed, and therefore statistical inference based on the results of this package, such as hypothesis testing and confidence intervals, can be performed using the normal distribution. Extensive simulations demonstrate the flexibility and correct implementation of the estimator. Two case studies performed with publicly available datasets demonstrate applicability of the package. In the Diabetic Retinopathy Study, the onset of blindness is clustered by patient, and in a large hard drive failure dataset, failure times are thought to be clustered by the hard drive manufacturer and model

The importance of scale economies and geographic diversification in community bank mergers

Mergers of community banks across economic market areas potentially reduce both idiosyncratic and local market risk. A merger may reduce idiosyncratic risk because the larger post-merger bank has a larger customer base. Negative credit and liquidity shocks from individual customers would have smaller effects on the portfolio of the merged entity than on the individual community banks involved in the merger. Geographic dispersion of banking activities across economic market areas may reduce local market risk because an adverse economic development that is unique to one market area will not affect a bank's loans to customers located in another market area. ; This paper simulates the mergers of community banks both within and across economic market areas by combining their call report data. We find that idiosyncratic risk reduction dominates local market risk reduction. In other words, a typical community bank can diversify away its idiosyncratic risk almost as completely by merging with a bank across the street as it can by merging with one located across the country. The bulk of the pure portfolio diversification effects for community banks, therefore, appear to be unrelated to diversification across market areas but, instead, are related to bank size. These findings help explain why many community banks have not pursued geographic diversification more aggressively, but they beg the question as to why more small community banks do not pursue in-market mergers.Bank supervision ; Bank mergers

Scale economies and geographic diversification as forces driving community bank mergers

Mergers of community banks across economic market areas potentially reduce both idiosyncratic and local market risk. Idiosyncratic risk may be reduced because the larger post merger bank has a larger customer base. Negative credit and liquidity shocks from individual customers would have smaller effects on the portfolio of the merged entity than on the individual community banks involved in the merger. Geographic dispersion of banking activities across economic market areas may reduce local market risk because an adverse economic development that is unique to one market area will not affect a bank*s loans to customers in different market areas. This paper simulates the mergers of community banks both within and across economic market areas by combining their call report data. We find that the potential for idiosyncratic risk reduction dominates the marginal contribution to risk reduction by diversifying across local markets. In other words, a typical community bank can reduce its insolvency risk about as much by merging with a bank across the street as it can by merging with one located across the country. The bulk of the pure portfolio diversification effects for community banks, therefore, appears to be unrelated to diversification across market areas and instead is related to bank size. These findings may help explain why many community banks have not pursued geographic diversification more aggressively, but they beg the question as to why more small community banks do not pursue in-market mergers.Community banks ; Bank mergers

A method for analyzing the performance aspects of the fault-tolerance mechanisms in FDDI

The ability of error recovery mechanisms to make the Fiber Distributed Data Interface (FDDI) satisfy real-time performance constraints in the presence of errors is analyzed. A complicating factor in these analyses is the rarity of the error occurrences, which makes direct simulation unattractive. Therefore, a fast simulation technique, called injection simulation, which makes it possible to analyze the performance of FDDI, including its fault tolerance behavior, was developed. The implementation of injection simulation for polling models of FDDI is discussed, along with simulation result