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In 1968 Shirley Chisholm became the first Black woman elected to the United States Congress. In 1972 she launched a campaign for the Democratic presidential nomination, breaking another political barrier.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
In statistics, sequential analysis or sequential hypothesis testing is statistical analysis where the sample size is not fixed in advance. Instead data is evaluated as it is collected, and further sampling is stopped in accordance with a pre-defined stopping rule as soon as significant results are observed. Thus a conclusion may sometimes be reached at a much earlier stage than would be possible with more classical hypothesis testing or estimation, at consequently lower financial and/or human cost.
The method of sequential analysis is first attributed to Abraham Wald[1] with Jacob Wolfowitz, W. Allen Wallis, and Milton Friedman[2][3] while at Columbia University's Statistical Research Group as a tool for more efficient industrial quality control during World War II. Its value to the war effort was immediately recognised, and led to its receiving a "restricted" classification.[4] At the same time, George Barnard led a group working on optimal stopping in Great Britain. Another early contribution to the method was made by K.J. Arrow with D. Blackwell and M.A. Girshick.[5]
A similar approach was independently developed from first principles at about the same time by Alan Turing, as part of the Banburismus technique used at Bletchley Park, to test hypotheses about whether different messages coded by German Enigma machines should be connected and analysed together. This work remained secret until the early 1980s.[6]
Peter Armitage introduced the use of sequential analysis in medical research, especially in the area of clinical trials. Sequential methods became increasingly popular in medicine following Stuart Pocock's work that provided clear recommendations on how to control Type 1 error rates in sequential designs.[7]
When researchers repeatedly analyze data as more observations are added, the probability of a Type 1 error increases. Therefore, it is important to adjust the alpha level at each interim analysis, such that the overall Type 1 error rate remains at the desired level. This is conceptually similar to using the Bonferroni correction, but because the repeated looks at the data are dependent, more efficient corrections for the alpha level can be used. Among the earliest proposals is the Pocock boundary. Alternative ways to control the Type 1 error rate exist, such as the Haybittle–Peto bounds, and additional work on determining the boundaries for interim analyses has been done by O'Brien & Fleming[8] and Wang & Tsiatis.[9]
A limitation of corrections such as the Pocock boundary is that the number of looks at the data must be determined before the data is collected, and that the looks at the data should be equally spaced (e.g., after 50, 100, 150, and 200 patients). The alpha spending function approach developed by Demets & Lan[10] does not have these restrictions, and depending on the parameters chosen for the spending function, can be very similar to Pocock boundaries or the corrections proposed by O'Brien and Fleming. Another approach that has no such restrictions at all is based on e-values and e-processes.
In a randomized trial with two treatment groups, group sequential testing may for example be conducted in the following manner: After n subjects in each group are available an interim analysis is conducted. A statistical test is performed to compare the two groups and if the null hypothesis is rejected the trial is terminated; otherwise, the trial continues, another n subjects per group are recruited, and the statistical test is performed again, including all subjects. If the null is rejected, the trial is terminated, and otherwise it continues with periodic evaluations until a maximum number of interim analyses have been performed, at which point the last statistical test is conducted and the trial is discontinued.[11]
Sequential analysis also has a connection to the problem of gambler's ruin that has been studied by, among others, Huygens in 1657.[12]
Step detection is the process of finding abrupt changes in the mean level of a time series or signal. It is usually considered as a special kind of statistical method known as change point detection. Often, the step is small and the time series is corrupted by some kind of noise, and this makes the problem challenging because the step may be hidden by the noise. Therefore, statistical and/or signal processing algorithms are often required. When the algorithms are run online as the data is coming in, especially with the aim of producing an alert, this is an application of sequential analysis.
Trials that are terminated early because they reject the null hypothesis typically overestimate the true effect size.[13] This is because in small samples, only large effect size estimates will lead to a significant effect, and the subsequent termination of a trial. Methods to correct effect size estimates in single trials have been proposed.[14] Note that this bias is mainly problematic when interpreting single studies. In meta-analyses, overestimated effect sizes due to early stopping are balanced by underestimation in trials that stop late, leading Schou & Marschner to conclude that "early stopping of clinical trials is not a substantive source of bias in meta-analyses".[15]
The meaning of p-values in sequential analyses also changes, because when using sequential analyses, more than one analysis is performed, and the typical definition of a p-value as the data “at least as extreme” as is observed needs to be redefined. One solution is to order the p-values of a series of sequential tests based on the time of stopping and how high the test statistic was at a given look, which is known as stagewise ordering,[13] first proposed by Armitage.
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Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
In statistics, sequential analysis or sequential hypothesis testing is statistical analysis where the sample size is not fixed in advance. Instead data is evaluated as it is collected, and further sampling is stopped in accordance with a pre-defined stopping rule as soon as significant results are observed. Thus a conclusion may sometimes be reached at a much earlier stage than would be possible with more classical hypothesis testing or estimation, at consequently lower financial and/or human cost.
In statistics, sequential estimation refers to estimation methods in sequential analysis where the sample size is not fixed in advance. Instead, data is evaluated as it is collected, and further sampling is stopped in accordance with a predefined stopping rule as soon as significant results are observed. The generic version is called the optimal Bayesian estimator, which is the theoretical underpinning for every sequential estimator (but cannot be instantiated directly). It includes a Markov process for the state propagation and measurement process for each state, which yields some typical statistical independence relations. The Markov process describes the propagation of a probability distribution over discrete time instances and the measurement is the information one has about each time instant, which is usually less informative than the state. Only the observed sequence will, together with the models, accumulate the information of all measurements and the corresponding Markov process to yield better estimates. From that, the Kalman filter (and its variants), the particle filter, the histogram filter and others can be derived. It depends on the models, which one to use and requires experience to choose the right one. In most cases, the goal is to estimate the state sequence from the measurements. In other cases, one can use the description to estimate the parameters of a noise process for example. One can also accumulate the unmodeled statistical behavior of the states projected in the measurement space (called innovation sequence, which naturally includes the orthogonality principle in its derivations to yield an independence relation and therefore can be also cast into a Hilbert space representation, which makes it very intuitive) over time and compare it with a threshold, which then corresponds to the aforementioned stopping criterion. One difficulty is to set up the initial conditions for the probabilistic models, which is in most cases done by experience, data sheets or precise measurements with a different setup. The statistical behaviour of the heuristic/sampling methods (e.g. particle filter or histogram filter) depends on many parameters and implementation details and should not be used in safety critical applications (since it is very hard to yield theoretical guarantees or do proper testing), unless one has a very good reason. If there is a dependence of each state on an overall entity (e.g. a map or simply an overall state variable), one typically uses SLAM (simultaneous localization and mapping) techniques, which include the sequential estimator as a special case (when the overall state variable has just one state). It will estimate the state sequence and the overall entity. There are also none-causal variants, that have all measurements at the same time, batches of measurements or revert the state evolution to go backwards again. These are then, however, not real time capable (except one uses a really big buffer, that lowers the throughput dramatically) anymore and only sufficient for post processing. Other variants do several passes to yield a rough estimate first and then refine it by the following passes, which is inspired by video editing/transcoding. For image processing (where all pixels are available at the same time) these methods become causal again. Sequential estimation is the core of many well known applications, such as the Viterbi decoder, convolutional codes, video compression or target tracking. Due to its state space representation, which is in most cases motivated by physical laws of motion, there is a direct link to control applications, which led to the use of the Kalman filter for space applications for example.
The sequential probability ratio test (SPRT) is a specific sequential hypothesis test, developed by Abraham Wald and later proven to be optimal by Wald and Jacob Wolfowitz. Neyman and Pearson's 1933 result inspired Wald to reformulate it as a sequential analysis problem. The Neyman-Pearson lemma, by contrast, offers a rule of thumb for when all the data is collected (and its likelihood ratio known). While originally developed for use in quality control studies in the realm of manufacturing, SPRT has been formulated for use in the computerized testing of human examinees as a termination criterion.
A Bellman equation, named after Richard E. Bellman, is a technique in dynamic programming which breaks an optimization problem into a sequence of simpler subproblems, as Bellman's "principle of optimality" prescribes. It is a necessary condition for optimality. The "value" of a decision problem at a certain point in time is written in terms of the payoff from some initial choices and the "value" of the remaining decision problem that results from those initial choices. The equation applies to algebraic structures with a total ordering; for algebraic structures with a partial ordering, the generic Bellman's equation can be used. The Bellman equation was first applied to engineering control theory and to other topics in applied mathematics, and subsequently became an important tool in economic theory; though the basic concepts of dynamic programming are prefigured in John von Neumann and Oskar Morgenstern's Theory of Games and Economic Behavior and Abraham Wald's sequential analysis. The term "Bellman equation" usually refers to the dynamic programming equation (DPE) associated with discrete-time optimization problems. In continuous-time optimization problems, the analogous equation is a partial differential equation called the Hamilton–Jacobi–Bellman equation. In discrete time any multi-stage optimization problem can be solved by analyzing the appropriate Bellman equation. The appropriate Bellman equation can be found by introducing new state variables (state augmentation). However, the resulting augmented-state multi-stage optimization problem has a higher dimensional state space than the original multi-stage optimization problem - an issue that can potentially render the augmented problem intractable due to the "curse of dimensionality". Alternatively, it has been shown that if the cost function of the multi-stage optimization problem satisfies a "backward separable" structure, then the appropriate Bellman equation can be found without state augmentation.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Shirley Chisholm, elected in 1968.
