Search results
Results From The WOW.Com Content Network
Risk is the lack of certainty about the outcome of making a particular choice. Statistically, the level of downside risk can be calculated as the product of the probability that harm occurs (e.g., that an accident happens) multiplied by the severity of that harm (i.e., the average amount of harm or more conservatively the maximum credible amount of harm).
More specifically, if the likelihood function is twice continuously differentiable on the k-dimensional parameter space assumed to be an open connected subset of , there exists a unique maximum ^ if the matrix of second partials [], =,, is negative definite for every at which the gradient [] = vanishes, and if the likelihood function approaches ...
PCA is a function of just the covariance matrix, and the first PCA pattern is defined so as to maximise explained variance; DCA is a function of the covariance matrix and a vector direction (the gradient of the impact function), and the first DCA pattern is defined so as to maximise probability density for a given value of the impact metric
the likelihood (probability) of occurrence of each consequence. Consequences are expressed numerically (e.g., the number of people potentially hurt or killed) and their likelihoods of occurrence are expressed as probabilities or frequencies (i.e., the number of occurrences or the probability of occurrence per unit time).
Risk assessment using qualifiers – estimate of risk associated with a particular hazard using qualifiers like high likelihood, low likelihood, etc Risk-based auditing – type of auditing which focuses upon the analysis and management of risks with the greatest potential impact Pages displaying wikidata descriptions as a fallback
Risk assessment is used for uncertain events that could have many outcomes and for which there could be significant consequences. Risk is a function of probability of an event (a particular hazard occurring) and the consequences given the event occurs. Probability refers to the likelihood that a hazard will occur.
The role of the Fisher information in the asymptotic theory of maximum-likelihood estimation was emphasized and explored by the statistician Sir Ronald Fisher (following some initial results by Francis Ysidro Edgeworth). The Fisher information matrix is used to calculate the covariance matrices associated with maximum-likelihood estimates.
Using this score function and Hessian matrix, the partial likelihood can be maximized using the Newton-Raphson algorithm. The inverse of the Hessian matrix, evaluated at the estimate of β, can be used as an approximate variance-covariance matrix for the estimate, and used to produce approximate standard errors for the regression coefficients.