Globally Robust Inference
... The robust approach to data analysis uses models that do not completely specify the distribution of the data, but rather assume that this distribution belongs to a certain neighborhood of a parametric model. Consequently, robust inference should be valid under all the distributions in these neighbor ...
... The robust approach to data analysis uses models that do not completely specify the distribution of the data, but rather assume that this distribution belongs to a certain neighborhood of a parametric model. Consequently, robust inference should be valid under all the distributions in these neighbor ...
transparency of financial time series.(Topic 1)
... Johann Heinrich Lambert (1728-1777) was a Swiss-German scientist and mathematician. He is generally recognized as the inventor of the time series graph, in which the values of some variable of interest are plotted against the vertical axis and time is plotted on the horizontal axis. William Playfair ...
... Johann Heinrich Lambert (1728-1777) was a Swiss-German scientist and mathematician. He is generally recognized as the inventor of the time series graph, in which the values of some variable of interest are plotted against the vertical axis and time is plotted on the horizontal axis. William Playfair ...
Chapter 8 – Confidence Intervals about a Single Parameter
... In any given situation, we have no way of knowing precisely how close the estimated value of a parameter is to the true value of the parameter. However, if the estimator satisfies the three properties listed above, we can be highly confident that the estimated parameter value is unlikely to differ f ...
... In any given situation, we have no way of knowing precisely how close the estimated value of a parameter is to the true value of the parameter. However, if the estimator satisfies the three properties listed above, we can be highly confident that the estimated parameter value is unlikely to differ f ...
Math 2 with Support - North Cobb High School Class Websites
... 4. The managers of a company with 500 employees want to know how the employees feel about some proposed changes. The managers use a computer to generate a list of 50 employees to survey from a database that includes all the employees. This is a random sample because a computer is randomly selecting ...
... 4. The managers of a company with 500 employees want to know how the employees feel about some proposed changes. The managers use a computer to generate a list of 50 employees to survey from a database that includes all the employees. This is a random sample because a computer is randomly selecting ...
Confidence intervals
... Using an estimated variance • When the σ² is not known but n ≥ 30 an unbiased estimate of the variance S2 can be ...
... Using an estimated variance • When the σ² is not known but n ≥ 30 an unbiased estimate of the variance S2 can be ...
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.