|
George Box
George Edward Pelham Box (18 October 1919 – 28 March 2013) was a British statistician, who worked in the areas of quality control, time-series analysis, design of experiments, and Bayesian inference. He has been called "one of the great statistical minds of the 20th century". He is famous for the quote "All models are wrong but some are useful". Education and early life He was born in Gravesend, Kent, England. Upon entering university he began to study chemistry, but was called up for service before finishing. During World War II, he performed experiments for the British Army exposing small animals to poison gas. To analyze the results of his experiments, he taught himself statistics from available texts. After the war, he enrolled at University College London and obtained a bachelor's degree in mathematics and statistics. He received a PhD from the University of London in 1953, under the supervision of Egon Pearson and Herman Otto Hartley, HO Hartley. Career and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gravesend, Kent
Gravesend is a town in northwest Kent, England, situated 21 miles (35 km) east-southeast of Charing Cross (central London) on the Bank (geography), south bank of the River Thames, opposite Tilbury in Essex. Located in the diocese of Rochester, it is the administrative centre of the borough of Gravesham. Gravesend marks the eastern limit of the Greater London Built-up Area, as defined by the UK Office for National Statistics. It had a population of 58,102 in 2021. Its geographical situation has given Gravesend strategic importance throughout the maritime history, maritime and History of communication, communications history of South East England. A Thames Gateway commuter town, it retains strong links with the River Thames, not least through the Port of London Authority Pilot Station, and has witnessed rejuvenation since the advent of High Speed 1 rail services via Gravesend railway station. The station was recently refurbished and has a new bridge. Name Recorded as Graves ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Inference
Bayesian inference ( or ) is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available. Fundamentally, Bayesian inference uses a prior distribution to estimate posterior probabilities. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability". Introduction to Bayes' rule Formal explanation Bayesian inference derives the posterior probability as a consequence of two antecedents: a prior probability and a "likelihood function" derive ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Shewhart Medal
The Shewhart Medal, named in honour of Walter A. Shewhart, is awarded annually by the American Society for Quality for ''...outstanding technical leadership in the field of modern quality control, especially through the development to its theory, principles, and techniques.'' The first medal was awarded in 1948. See also * List of mathematics awards * Wilks Memorial Award The Wilks Memorial Award is awarded by the American Statistical Association to recognize outstanding contributions to statistics. It was established in 1964 and is awarded yearly. It is named in memory of the statistician Samuel S. Wilks. The awa ... References {{reflist External linksOfficial website Awards established in 1948 Statistical awards ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ljung–Box Test
The Ljung–Box test (named for Greta M. Ljung and George E. P. Box) is a type of statistical test of whether any of a group of autocorrelations of a time series are different from zero. Instead of testing randomness at each distinct lag, it tests the "overall" randomness based on a number of lags, and is therefore a portmanteau test. This test is sometimes known as the Ljung–Box Q test, and it is closely connected to the Box–Pierce test (which is named after George E. P. Box and David A. Pierce). In fact, the Ljung–Box test statistic was described explicitly in the paper that led to the use of the Box–Pierce statistic, and from which that statistic takes its name. The Box–Pierce test statistic is a simplified version of the Ljung–Box statistic for which subsequent simulation studies have shown poor performance. The Ljung–Box test is widely applied in econometrics and other applications of time series analysis. A similar assessment can be also carried out with the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Box's M Test
Box's ''M'' test is a multivariate statistical test used to check the equality of multiple variance-covariance matrices. The test is commonly used to test the assumption of homogeneity of variances and covariances in MANOVA and linear discriminant analysis. It is named after George E. P. Box, who first discussed the test in 1949. The test uses a chi-squared approximation. Box's ''M'' test is susceptible to errors if the data does not meet model assumptions or if the sample size is too large or small. Box's ''M'' test is especially prone to error if the data does not meet the assumption of multivariate normality. See also * Bartlett's test * Levene's test In statistics, Levene's test is an inferential statistic used to assess the equality of variances for a variable calculated for two or more groups. This test is used because some common statistical procedures assume that variances of the population ... References Multivariate statistics Statistical tests {{statist ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Box–Muller Transform
The Box–Muller transform, by George Edward Pelham Box and Mervin Edgar Muller, is a random number sampling method for generating pairs of independent, standard, normally distributed (zero expectation, unit variance) random numbers, given a source of uniformly distributed random numbers. The method was first mentioned explicitly by Raymond E. A. C. Paley and Norbert Wiener in their 1934 treatise on Fourier transforms in the complex domain. Given the status of these latter authors and the widespread availability and use of their treatise, it is almost certain that Box and Muller were well aware of its contents. The Box–Muller transform is commonly expressed in two forms. The basic form as given by Box and Muller takes two samples from the uniform distribution on the interval and maps them to two standard, normally distributed samples. The polar form takes two samples from a different interval, , and maps them to two normally distributed samples without the use of sine or ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Box–Cox Distribution
In statistics, the Box–Cox distribution (also known as the power-normal distribution) is the distribution of a random variable ''X'' for which the Box–Cox transformation on ''X'' follows a truncated normal distribution. It is a continuous probability distribution having probability density function (pdf) given by : f(y) = \frac \exp\left\ for ''y'' > 0, where ''m'' is the location parameter of the distribution, ''s'' is the dispersion, ''ƒ'' is the family parameter, ''I'' is the indicator function, Φ is the cumulative distribution function of the standard normal distribution, and sgn is the sign function. Special cases * ''ƒ'' = 1 gives a truncated normal distribution In probability and statistics, the truncated normal distribution is the probability distribution derived from that of a normally distributed random variable by bounding the random variable from either below or above (or both). The truncated no .... References * Continuous distribution ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Power Transform
In statistics, a power transform is a family of functions applied to create a monotonic transformation of data using power functions. It is a data transformation technique used to stabilize variance, make the data more normal distribution-like, improve the validity of measures of association (such as the Pearson correlation In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviation ... between variables), and for other data stabilization procedures. Power transforms are used in multiple fields, including Multiresolution analysis, multi-resolution and wavelet analysis, statistical data analysis, medical research, modeling of physical processes, Geochemical modeling, geochemical data analysis, epidemiology and many other clinical, environmental and social research areas. Definition The power tr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Time Series Analysis
In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. A time series is very frequently plotted via a run chart (which is a temporal line chart). Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements. Time series ''analysis'' comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series ''forec ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
