Anti-CPD (aa1169-1374) polyclonal antibody

A global uncertainty environment, such as the COVID-19 pandemic, has affected the manufacturing industry severely in terms of supply and demand balancing. So, it is common that one stage statistical process control (SPC) chart affects the next-stage SPC chart. It is our research objective to consider a conditional case for the multi-stage multivariate change point detection (CPD) model for highly correlated multivariate data via copula conditional distributions with principal component analysis (PCA) and functional PCA (FPCA). First of all, we review the current available multivariate CPD models, which are the energy test-based control chart (ETCC) and the nonparametric multivariate change point model (NPMVCP). We extend the current available CPD models to the conditional multi-stage multivariate CPD model via copula conditional distributions with PCA for linear normal multivariate data and FPCA for nonlinear non-normal multivariate data.

Counting experiments often rely on Monte Carlo simulations for predictions of Poisson expectations. The accompanying uncertainty from the finite Monte Carlo sample size can be incorporated into parameter estimation by modifying the Poisson likelihood. We first review previous frequentist methods of this type by Barlow et al, Bohm et al, and Chirkin, as well as recently proposed probabilistic methods by the author and Arguelles et al. We show that all these approaches can be understood in a unified way: they all approximate the underlying probability distribution of the sum of weights in a given bin, the compound Poisson distribution (CPD). The Probabilistic methods marginalize the Poisson mean with a distribution that approximates the CPD, while the frequentist counterparts optimize the same integrand treating the mean as a nuisance parameter. With this viewpoint we can motivate three new probabilistic likelihoods based on generalized gamma-Poisson mixture distributions which we derive in analytic form. Afterwards, we test old and new formulas in different parameter estimation settings consisting of a "background" and "signal" dataset. The probablistic counterpart to the Ansatz by Barlow et al. outperforms all other existing approaches in various scenarios. We further find a surprising outcome: usage of the exact CPD is actually bad for parameter estimation. A continuous approximation performs much better and in principle allows to perform bias-free inference at any level of simulated livetime if the first two moments of the CPD of each dataset are known exactly. Finally, we also discuss the situation where new Monte Carlo simulation is produced for a given parameter choice which leads to fluctuations in the computed likelihood value. Two of the new formulas allow to include this Poisson uncertainty directly into the likelihood which substantially decreases these fluctuations.