# The Huggins-Roy family of effective sample sizes

When we work with weighted samples, it’s essential to introduce adjustments for the sample size. Indeed, let’s consider two following weighted samples:

\[\mathbf{x}_1 = \{ x_1, x_2, \ldots, x_n \}, \quad \mathbf{w}_1 = \{ w_1, w_2, \ldots, w_n \}, \]

\[\mathbf{x}_2 = \{ x_1, x_2, \ldots, x_n, x_{n+1} \}, \quad \mathbf{w}_2 = \{ w_1, w_2, \ldots, w_n, 0 \}. \]

Since the weight of \(x_{n+1}\) in the second sample is zero,
it’s natural to expect that both samples have the same set of properties.
However, there is a major difference between \(\mathbf{x}_1\) and \(\mathbf{x}_2\): their sample sizes which are
\(n\) and \(n+1\).
In order to eliminate this difference, we typically introduce the *effective sample size* (ESS)
which is estimated based on the list of weights.

There are various ways to estimate the ESS. In this post, we briefly discuss the Huggins-Roy’s family of ESS.

For a list of weights \(\mathbf{w} = \{ w_1, w_2, \ldots, w_n \}\), we can consider the corresponding normalized weights (or standardized weights):

\[\overline{\mathbf{w}} = \frac{\mathbf{w}}{\sum_{i=1}^n w_i}. \]

For any non-degenerate weighted sample, the sum of all weights is always positive so that the normalized weights are defined.

The Huggins-Roy’s family is given by:

\[\operatorname{ESS}_\beta(\overline{\mathbf{w}}) = \Bigg( \frac{1}{\sum_{i=1}^n \overline{w}_i^\beta } \Bigg)^{\frac{1}{\beta - 1}} = \Bigg( \sum_{i=1}^n \overline{w}_i^\beta \Bigg)^{\frac{1}{1 - \beta}}. \]

This family is proposed in [Huggins2019] and discussed in [Elvira2021] and [Elvira2022].

In order to understand this approach, let’s consider several special cases.

- \(\beta = 0\):

\[\operatorname{ESS}_0(\overline{\mathbf{w}}) = n - n_z(\overline{\mathbf{w}}), \]

where \(n_z(\overline{\mathbf{w}})\) is the number of zeros in \(\overline{\mathbf{w}}\). This approach is quite straightforward: we just omit elements with zero weights.

- \(\beta = 1/2\):

\[\operatorname{ESS}_{1/2}(\overline{\mathbf{w}}) = \Bigg( \sum_{i=1}^n \sqrt{\overline{w}_i} \Bigg)^2. \]

- \(\beta = 1\):

\[\operatorname{ESS}_{1}(\overline{\mathbf{w}}) = \operatorname{exp} \Bigg( -\sum_{i=1}^n \overline{w}_i \log \overline{w}_i \Bigg)^2. \]

This approach is also known as *perplexity*

- \(\beta = 2\):

\[\operatorname{ESS}_{2}(\overline{\mathbf{w}}) = \frac{1}{\sum_{i=1}^n \overline{w}_i^2 }. \]

This approach is often referenced as the Kish’s effective sample size (see [Kish1965]).

- \(\beta = \infty\):

\[\operatorname{ESS}_{\infty}(\overline{\mathbf{w}}) = \frac{1}{\max [\overline{w}_1, \overline{w}_2, \ldots, \overline{w}_n] }. \]

This approach is also popular and straightforward: we define the sample size based on the maximum weight.

### References

**[Elvira2022]**

Víctor Elvira, Luca Martino, and Christian P. Robert. “Rethinking the effective sample size.” International Statistical Review (2022).

https://arxiv.org/pdf/1809.04129.pdf**[Elvira2021]**

Víctor Elvira, Luca Martino. “Effective sample size approximations as entropy measures.” (2021)

https://vixra.org/pdf/2111.0145v1.pdf**[Huggins2019]**

Huggins, Jonathan H., and Daniel M. Roy. “Sequential Monte Carlo as approximate sampling: bounds, adaptive resampling via \(\infty\)-ESS, and an application to particle Gibbs.” Bernoulli 25, no. 1 (2019): 584-622.

https://arxiv.org/pdf/1503.00966.pdf**[Kish1965]**

Kish, Leslie. Survey sampling. Chichester., 1965.

https://doi.org/10.1002/bimj.19680100122