839 research outputs found
Exact anytime-valid confidence intervals for contingency tables and beyond
Get PDFE-variables are tools for retaining type-I error guarantee with optional stopping. We extend E-variables for sequential two-sample tests to general null hypotheses and anytime-valid confidence sequences. We provide implementations for estimating risk difference, relative risk and odds-ratios in contingency tables
Anytime-valid confidence intervals for contingency tables and beyond
Get PDFE variables are tools for designing tests that keep their type-I error
guarantees under flexible sampling scenarios such as optional stopping and
continuation. We extend the recently developed E variables for two-sample tests
to general null hypotheses and the corresponding anytime-valid confidence
sequences. Using the 2x2 contingency table (Bernoulli) setting as a running
example, we provide simple implementations of these confidence sequences for
linear and odds-ratio based effect size
Safe sequential testing and effect estimation in stratified count data
Get PDFSequential decision making significantly speeds up research and is more cost-effective compared to fixed-n methods. We present a method for sequential decision making for stratified count data that retains Type-I error guarantee or false discovery rate under optional stopping, using e-variables. We invert the method to construct stratified anytime-valid confidence sequences, where cross-talk between subpopulations in the data can be allowed during data collection to improve power. Finally, we combine information collected in separate subpopulations through pseudo-Bayesian averaging and switching to create effective estimates for the minimal, mean and maximal treatment effects in the subpopulations
Anytime-valid testing and confidence intervals in contingency tables and beyond
Get PDFWe develop sequential A/B tests with strict Type-I error control under optional stopping. Our tests are based on E-variables, which recently have turned out be successful tools for anytime-valid inference. We introduce a general method for constructing E-variables that can be used for A/B testing in 2-sample streams. In contrast to earlier methods developed in the sequential testing literature, our approach is valid for both balanced and unbalanced experiments and allows for arbitrary, user-specified notions of effect size. The same method can be used to design anytime-valid confidence sequences to estimate effect sizes in data streams. With two Bernoulli streams as a running example, we illustrate the power of our A/B test and show that decisions can often be made earlier compared to classical methods, such as Fisher's exact test. We also illustrate the confidence sequences with two different notions of effect size: log odds ratio and difference in mean
Communication and trust in the bounded confidence model
Full text linkThe communication process in a situation of emergency is discussed within the
Scheff theory of shame and pride. The communication involves messages from
media and from other persons. Three strategies are considered: selfish (to
contact friends), collective (to join other people) and passive (to do
nothing). We show that the pure selfish strategy cannot be evolutionarily
stable. The main result is that the community structure is statistically
meaningful only if the interpersonal communication is weak.Comment: 6 pages, 5 figures, RevTeX, for ICCCI-201
A cohort study of post-weaning multisystemic wasting syndrome and PCV2 in 178 pigs from birth to 14 weeks on a single farm in England
Get PDFOur hypothesis was that pigs that develop post-weaning multisystemic wasting syndrome (PMWS) are detectable from an early age with signs of weight loss and other clinical and serological abnormalities. Therefore, the objective of this study was to investigate the temporally varying and fixed events linked with the clinical incidence of PMWS by comparing affected and unaffected pigs in a cohort of 178 male piglets. Piglets were enrolled at birth and examined each week. Samples of blood were collected at regular intervals. The exposures measured were porcine circovirus type 2 (PCV2) antibody titres in all 178 and PCV2 antigen in a subset of 75 piglets. We also observed piglet health and measured their weight, and a post-mortem examination was performed by an external laboratory on all pigs between 6 and 14 weeks of age that died. From the cohort, 14 (8%) pigs died from PMWS and 4% from other causes. A further 37 pigs between 6 and 14 weeks of age died from PMWS (30) and ileitis and other causes (7). PMWS was only apparent in pigs from 1 to 2 weeks before death when they wasted rapidly. There were no other characteristic clinical signs and no obvious gross clinical lesions post-mortem. There was no strong link with PCV2 antibody throughout life but PCV2 antigen level was higher from 4 to 6 weeks of age in pigs that died from PMWS compared with pigs that died from other causes
Safe Sequential Conditional Independence Tests for Discrete Variables
Get PDFWe present tests for conditional independence of discrete variables that can be applied in a sequential setting with Type-I error probability guarantee. Power of these tests can be improved by incorporating hypothesized effect size or by sharing information between strata. Both scenarios are illustrated through simulations
Generic E-Variables for Exact Sequential k-Sample Tests that allow for Optional Stopping
Get PDFWe develop E-variables for testing whether two or more data streams come from the same source or not, and more generally, whether the difference between the sources is larger than some minimal effect size. These E-variables lead to exact, nonasymptotic tests that remain safe, i.e. keep their type-I error guarantees, under flexible sampling scenarios such as optional stopping and continuation. In special cases our E-variables also have an optimal 'growth' property under the alternative. While the construction is generic, we illustrate it through the special case of k x 2 contingency tables, where we also allow for the incorporation of different restrictions on a composite alternative. Comparison to p-value analysis in simulations and a real-world example show that E-variables, through their flexibility, often allow for early stopping of data collection, thereby retaining similar power as classical methods, while also retaining the option of extending or combining data afterwards
Two-sample tests that are safe under optional stopping
Get PDFWe develop E variables for testing whether two data streams come from the same source or not, and more generally, whether the difference between the sources is larger than some minimal effect size. These E variables lead to tests that remain safe, i.e. keep their Type-I error guarantees, under flexible sampling scenarios such as optional stopping and continuation. In special cases our E variables also have an optimal `growth' property under the alternative. We illustrate the generic construction through the special case of 2x2 contingency tables, where we also allow for the incorporation of different restrictions on a composite alternative. Comparison to p-value analysis in simulations and a real-world example show that E variables, through their flexibility, often allow for early stopping of data collection, thereby retaining similar power as classical methods
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