**Exponential Moving Average**

An exponential moving average (EMA), also known as an exponentially weighted moving average (EWMA), is a type of infinite impulse response filter that applies weighting factors which decrease exponentially. The weighting for each older datum decreases exponentially, never reaching zero. The graph at right shows an example of the weight decrease. …

Exponential Moving Average (EMA) **Pruned Exact Linear Time**

This approach is based on the algorithm of Jackson et al. (2005 (‘An algorithm for optimal partitioning of data on an interval’)) , but involves a pruning step within the dynamic program. This pruning reduces the computational cost of the method, but does not affect the exactness of the resulting segmentation. It can be applied to find changepoints under a range of statistical criteria such as penalised likelihood, quasi-likelihood (Braun et al., 2000 (‘Multiple changepoint fitting via quasilikelihood, with application to DNA sequence segmentation’)) and cumulative sum of squares (Inclan and Tiao, 1994 (‘Use of cumulative sums of squares for retrospective detection of changes of variance.’); Picard et al., 2011 (‘Joint segmentation, calling and normalization of multiple cgh profiles’)). In simulations we compare PELT with both Binary Segmentation and Optimal Partitioning. We show that PELT can be calculated orders of magnitude faster than Optimal Partitioning, particularly for long data sets. Whilst asymptotically PELT can be quicker, we find that in practice Binary Segmentation is quicker on the examples we consider, and we believe this would be the case in almost all applications. However, we show that PELT leads to a substantially more accurate segmentation than Binary Segmentation. …

Pruned Exact Linear Time (PELT) **Negative Binomial Regression**

Negative binomial regression is for modeling count variables, usually for over-dispersed count outcome variables. …

Negative Binomial Regression (NBR)

# If you did not already know

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May 2017

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