Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure

Dalal, Siddhartha R.; Fowlkes, Edward B.; Hoaddley, Bruce

doi:10.2307/2290069

Cited by 58 publications

(49 citation statements)

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“…This cause of bias received much infamy in the statistical literature following the loss of the space shuttle Challenger in January 1986. Dalal et al (1989) revealed that the loss was due to cold-weather failure of O-ring seals between the joints of one of the solid-fuel rocket motors. The high risk of such failure at the launch temperature of approximately À18C was not identified because analyses of the relationship between temperature and O-ring performance (recorded from previous flights) omitted the zero data points for which there was no measured thermal distress.…”

Section: Exclude the Zerosmentioning

confidence: 99%

Much ado about nothings: using zero similarity points in distance-decay curves

Millar

Anderson

Tolimieri

2011

Ecology

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Abstract. Distance decay is used to describe the (usually exponential) decay in ecological similarity of assemblages between two sites as a function of their distance apart along an environmental gradient. Exponential distance-decay curves are routinely fitted by calculating the ecological similarity between each pair of sites, and fitting a linear regression to the points on a scatter plot of log-similarity vs. distance (x-axis). However, pairs of sites where the assemblages have no species in common pose a problem, because the similarity is zero, and the log transformation cannot be applied. Common fixes to this problem (i.e., either removing or transforming the zero values) are shown to have undesirable consequences and to give widely disparate estimates. A new method is presented as a special case of a generalized dissimilarity model. It is fitted very quickly and easily using existing software, and it does not require removal or transformation of the zero similarity points. Its simplicity makes it convenient for use in conjunction with the resampling methods that are routinely employed to test hypotheses, to obtain standard errors of estimated parameters, or to compare distance-decay curves. A word of caution about standard application of the bootstrap is noted, and modified bootstrap and jackknife alternatives are demonstrated.

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Section: Exclude the Zerosmentioning

confidence: 99%

Much ado about nothings: using zero similarity points in distance-decay curves

Millar

Anderson

Tolimieri

2011

Ecology

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“…On the morning of this catastrophic accident, the O-rings were 22 1F below the minimum temperature recorded in all the previous flights. There has been a large discussion in the literature about how to conduct a scientific risk analysis which allows one to predict the O-rings failure/success from its temperature (see, for instance, Dalal et al, 1989;Maranzano and Krzysztofowicz, 2008). The simplest possibility is by a logistic regression model where the variables of interest are the temperature of the primary O-rings of the space shuttle and an indicator of failure/ success of the O-rings during takeoff; the data can be found in Christensen (1990, Section 2.6).…”

Section: This Yieldsmentioning

confidence: 99%

“…An important instance of this method, to which we devote some attention, is the stochastic zigzag method-an algorithm largely inspired in the works of Mexia et al (1999) and Pereira and Mexia (2010). We apply this instance of our method to obtain maximum likelihood estimates in a classical problem of statistical risk modeling related to NASA's (National Aeronautics and Space Administration) first shuttle tragedy (Dalal et al, 1989). This paper is structured as follows.…”

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confidence: 99%

A generalization of the Solis–Wets method

Carvalho

2012

Journal of Statistical Planning and Inference

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a b s t r a c tIn this paper we focus on the application of global stochastic optimization methods to extremum estimators. We propose a general stochastic method-the master method -which includes several stochastic optimization algorithms as a particular case. The proposed method is sufficiently general to include the Solis-Wets method, the improving hit-and-run algorithm, and a stochastic version of the zigzag algorithm. A matrix formulation of the master method is presented and some specific results are given for the stochastic zigzag algorithm. Convergence of the proposed method is established under a mild set of conditions, and a simple regression model is used to illustrate the method.

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“…About 119 years ago, it was first introduced by de Moivre not by a French Probabilist Poisson, though the distribution is named Poisson. The Poisson distribution is frequently employed to explain uncertainty in count data such as the medication errors, adverse events, radioactive decay, traffic congestion, molecular mutations, ambulatory pickups of patients from their home as long as the data are about rarity (Cameron and Trivedi, 1986;Dalal et al, 1989;Davutyan, 1989;Deb and Trivedi, 1997;Winkelmann and Zimmermann, 1994;Thakur et al, 1980). For a chance mechanism to be governed by a Poisson distribution, three are assumptions which should be validated.…”

Section: Introductionmentioning

confidence: 99%

Is Poisson Dispersion Diluted or Over-Saturated? An Index Is Created to Answer

Shanmugam¹

2012

American Journal of Biostatistics

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A prelude to interpret a pattern in the repeating incidences is to identify the underlying frequency distribution of the collected data. A case in point is the Poisson distribution which is often selected for medical count data such as gene mutations, medication error and number of ambulatory pickups in a day. A requirement for the Poisson distribution is that the variance ought to be equal to the mean. The variance signifies the volatility in the occurrences. An implication is that the volatility becomes more when the average incidence is higher. When this requirement of the functional equivalence of the Poisson mean and variance is breached, the data deviates from a Poisson distribution. How could a data analyst recognize and point out to the medical team the dilution level of the requirement in their data? For this purpose, a simple and easier geometrical approach is developed in this article and illustrated with several historical data sets in the literature.

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Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure

Cited by 58 publications

References 0 publications

Much ado about nothings: using zero similarity points in distance-decay curves

Much ado about nothings: using zero similarity points in distance-decay curves

A generalization of the Solis–Wets method

Is Poisson Dispersion Diluted or Over-Saturated? An Index Is Created to Answer

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