nonparametric method supply chain probability risk

#NONPARAMETRIC METHOD SUPPLY CHAIN PROBABILITY RISK SERIES#

For a correct analysis of mixed paired and unpaired

Health of the person and an analysis that considered ulcers as independent observations would be incorrect.

We might have 20 subjects withģ0 ulcers but the number of independent pieces of information is 20 because the state of ulcers on each leg for one person may be influenced by the state of For example, suppose we were looking at treatment of leg ulcers, in which some people had an ulcer on each leg. One of the most common mistakes in statistical analysis is to treat correlated variables as if they were

Results measured over time require special care.

Analysis should reflect the design, and so a matched design should be followed by a matched analysis.

Thus results from a crossover trial, or from a case-control study in which the controls were matched to the cases by age, sex and social class, are not independent. The investigator should then ask 'are the data independent?' This can be difficult to decide but as a rule of thumb results on the same individual, or from matched individuals, are not independent. one that is unlikely to reject the null hypothesis. Note that, since tests are rarely independent, this is a very conservative procedure – i.e. Thus if there were two independent hypotheses a result would be declared significant only if P<0.025. A useful guide is to use a Bonferroni correction, which states simply that if one is testing n independent hypotheses, one should use a significance level of 0.05/ n. Although it is valid to use statistical tests on hypotheses suggested by the data, the P values should be used only as guidelines, and the results treated as tentative until confirmed by subsequent studies. A sensible plan is to limit severely the number of confirmatory hypotheses.

#NONPARAMETRIC METHOD SUPPLY CHAIN PROBABILITY RISK SERIES#

No single study can support a whole series of hypotheses. It is important to decide a priori which hypotheses are confirmatory (that is, are testing some presupposed relationship), and which are exploratory (are suggested by the data). If there is no hypothesis, then there is no statistical test. For example, in a prevalence study there is no hypothesis to test, and the size of the study is determined by how accurately the investigator wants to determine the prevalence. In terms of selecting a statistical test, the most important question is 'what is the main study hypothesis?' In some cases there is no hypothesis the investigator just wants to 'see what is there'. Statistics: Parametric and non-parametric tests A sample is drawn from the data set, its VaR recorded, and the data “returned.” This procedure is repeated over and over.Parametric and Non-parametric tests for comparing two or more groups It assumes that the distribution of returns will remain the same in the past and in the future, justifying the use of historical returns to forecast the VaR.Ī bootstrap procedure involves resampling from our existing data set with replacement. It is employed in the estimation of VaR and ES. The Bootstrap Historical Simulation Approach to Estimating Coherent Risk Measuresīootstrapping presents a simple but powerful improvement over basic Historical Simulation. Identify the advantages and disadvantages of nonparametric estimation methods.Compare and contrast the age-weighted, the volatility-weighted, the correlation-weighted, and the filtered historical simulation approaches.Describe historical simulation using non-parametric density estimation.

Apply the bootstrap historical simulation approach to estimate coherent risk measures.

P> After completing this reading, you should be able to:

Nonparametric method supply chain probability risk