English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We consider a sequence of variables having multinomial distribution with thenumber of trials corresponding to these variables being large and possiblydifferent. The multinomial probabilities of the categories are assumed to varyrandomly depending on batches. The proposed framework is interesting from theperspective of various applications in practice such as predicting the winnerof an election, forecasting the market share of different brands etc. In thiswork, first we derive sufficient conditions of asymptotic normality of theestimates of the multinomial cell probabilities, and corresponding suitabletransformations. Then, we consider a Bayesian setting to implement our model.We consider hierarchical priors using multivariate normal and inverse Wishartdistributions, and establish the posterior consistency. Based on this resultand following appropriate Gibbs sampling algorithms, we can infer aboutaggregate data. The methodology is illustrated in detail with two real lifeapplications, in the contexts of political election and sales forecasting.Additional insights of effectiveness are also derived through a simulationstudy.

Modeling a sequence of multinomial data with randomly varying probabilities