Directional Statistics
... The uniform distribution on hyperspheres • The CLT implies that for a sequence X1 , . . . , Xn of i.i.d unit ...
... The uniform distribution on hyperspheres • The CLT implies that for a sequence X1 , . . . , Xn of i.i.d unit ...
Parameter Learning in General Equilibrium: The Asset Pricing Implications
... model is too simple along many dimensions to be considered realistic, the learning dynamics reveal a number of interesting …ndings. We …nd that parameter uncertainty has a quantitatively large and long-lasting impact on the equity premium. As a benchmark, the average excess return on a levered consu ...
... model is too simple along many dimensions to be considered realistic, the learning dynamics reveal a number of interesting …ndings. We …nd that parameter uncertainty has a quantitatively large and long-lasting impact on the equity premium. As a benchmark, the average excess return on a levered consu ...
Bayesian analysis
... Now, we’re looking for a mathematical statement of what we know and only what we know. For this idea to properly grounded requires a sense of complete ignorance (even though this may never represent out state of background knowledge). For instance, if we think that µ1 is more likely the mean or exp ...
... Now, we’re looking for a mathematical statement of what we know and only what we know. For this idea to properly grounded requires a sense of complete ignorance (even though this may never represent out state of background knowledge). For instance, if we think that µ1 is more likely the mean or exp ...
Estimating Network Layer Subnet Characteristics via Statistical Sampling
... neighboring subnets of a subnet. Figure 2 shows an example target domain such that large circles depict the subnets and small circles filled in gray denote the IP addresses hosted by the subnets. Considering subnets being sampled through IP addresses, S3 , in Figure 2, is twice as likely to be obser ...
... neighboring subnets of a subnet. Figure 2 shows an example target domain such that large circles depict the subnets and small circles filled in gray denote the IP addresses hosted by the subnets. Considering subnets being sampled through IP addresses, S3 , in Figure 2, is twice as likely to be obser ...
Single-crystal structure refinements and crystal chemistry of
... To study structural changes with changing chemical composition in the octahedral sheet of trioctahedral potassium-rich 1M micas, 3 natural and 12 synthetic micas were examined by singlecrystal X-ray diffraction. Samples with Ni2+, Mg2+, Co2+, Fe2+, and Al3+ in the octahedral sheet and with Si4+, Al3 ...
... To study structural changes with changing chemical composition in the octahedral sheet of trioctahedral potassium-rich 1M micas, 3 natural and 12 synthetic micas were examined by singlecrystal X-ray diffraction. Samples with Ni2+, Mg2+, Co2+, Fe2+, and Al3+ in the octahedral sheet and with Si4+, Al3 ...
AP® Statistics 2010 Scoring Guidelines - AP Central
... selected in the future will produce an interval that captures the actual value of the population proportion of households in the county that have at least one dog. Partially correct (P) if the student provides an interpretation of the confidence level that includes two of the three components requir ...
... selected in the future will produce an interval that captures the actual value of the population proportion of households in the county that have at least one dog. Partially correct (P) if the student provides an interpretation of the confidence level that includes two of the three components requir ...
Bootstrapping (statistics)
In statistics, bootstrapping can refer to any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Generally, it falls in the broader class of resampling methods.Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution function of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples with replacement, of the observed dataset (and of equal size to the observed dataset).It may also be used for constructing hypothesis tests. It is often used as an alternative to statistical inference based on the assumption of a parametric model when that assumption is in doubt, or where parametric inference is impossible or requires complicated formulas for the calculation of standard errors.