**brglm2** provides tools for the estimation and inference from generalized linear models using various methods for bias reduction or maximum penalized likelihood with powers of the Jeffreys prior as penalty. Reduction of estimation bias is achieved either through the mean-bias reducing adjusted score equations in Firth (1993) and I. Kosmidis and Firth (2009), or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as prescribed in Cordeiro and McCullagh (1991), or through the median-bias reducing adjusted score equations in Kenne Pagui, Salvan, and Sartori (2017).

In the special case of generalized linear models for binomial, Poisson and multinomial responses (both nominal and ordinal), mean and median bias reduction and maximum penalized likelihood return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite, like in complete and quasi-complete separation as defined in Albert and Anderson (1984).

The workhorse function is `brglmFit()`

, which can be passed directly to the `method`

argument of the `glm`

function. `brglmFit`

implements a quasi-Fisher scoring procedure, whose special cases result in various explicit and implicit bias reduction methods for generalized linear models (the classification of bias reduction methods into explicit and implicit is given in I. Kosmidis 2014).

This vignette

- presents the supported bias-reducing adjustments to the score functions for generalized linear models
- describes the fitting algorithm at the core of
**brglm2**

The bias-reducing quasi Fisher scoring iteration is also described in detail in the bias vignette of the **enrichwith** R package. I. Kosmidis and Firth (2010) describe a parallel quasi Newton-Raphson procedure.

Most of the material in this vignette comes from a presentation by Ioannis Kosmidis at the useR! 2016 international conference at University of Stanford on 16 June 2016. The presentation was titled “Reduced-bias inference in generalized linear models” and can be watched online at this link.

Suppose that \(y_1, \ldots, y_n\) are observations on independent random variables \(Y_1, \ldots, Y_n\), each with probability density/mass function of the form \[ f_{Y_i}(y) = \exp\left\{\frac{y \theta_i - b(\theta_i) - c_1(y)}{\phi/m_i} - \frac{1}{2}a\left(-\frac{m_i}{\phi}\right) + c_2(y) \right\} \] for some sufficiently smooth functions \(b(.)\), \(c_1(.)\), \(a(.)\) and \(c_2(.)\), and fixed observation weights \(m_1, \ldots, m_n\). The expected value and the variance of \(Y_i\) are then \[\begin{align*} E(Y_i) & = \mu_i = b'(\theta_i) \\ Var(Y_i) & = \frac{\phi}{m_i}b''(\theta_i) = \frac{\phi}{m_i}V(\mu_i) \end{align*}\] Hence, in this parameterization, \(\phi\) is a dispersion parameter.

A generalized linear model links the mean \(\mu_i\) to a linear predictor \(\eta_i\) as \[ g(\mu_i) = \eta_i = \sum_{t=1}^p \beta_t x_{it} \] where \(g(.)\) is a monotone, sufficiently smooth link function, taking values on \(\Re\), \(x_{it}\) is the \((i,t)th\) component of a model matrix \(X\), and \(\beta = (\beta_1, \ldots, \beta_p)^\top\).

Suppressing the dependence of the various quantities on the model parameters and the data, the derivatives of the log-likelihood about \(\beta\) and \(\phi\) (score functions) are \[\begin{align*} s_\beta & = \frac{1}{\phi}X^TWD^{-1}(y - \mu) \\ s_\phi & = \frac{1}{2\phi^2}\sum_{i = 1}^n (q_i - \rho_i) \end{align*}\] with \(y = (y_1, \ldots, y_n)^\top\), \(\mu = (\mu_1, \ldots, \mu_n)^\top\), \(W = {\rm diag}\left\{w_1, \ldots, w_n\right\}\) and \(D = {\rm diag}\left\{d_1, \ldots, d_n\right\}\), where \(w_i = m_i d_i^2/v_i\) is the \(i\)th working weight, with \(d_i = d\mu_i/d\eta_i\) and \(v_i = V(\mu_i)\). Furthermore, \(q_i = -2 m_i \{y_i\theta_i - b(\theta_i) - c_1(y_i)\}\) and \(\rho_i = m_i a'_i\) with \(a'_i = a'(-m_i/\phi)\). The expected information matrix about \(\beta\) and \(\phi\) is \[ i = \left[ \begin{array}{cc} i_{\beta\beta} & 0_p \\ 0_p^\top & i_{\phi\phi} \end{array} \right] = \left[ \begin{array}{cc} \frac{1}{\phi} X^\top W X & 0_p \\ 0_p^\top & \frac{1}{2\phi^4}\sum_{i = 1}^n m_i^2 a''_i \end{array} \right]\,, \] where \(0_p\) is a \(p\)-vector of zeros, and \(a''_i = a''(-m_i/\phi)\).

The maximum likelihood estimators \(\hat\beta\) and \(\hat\phi\) of \(\beta\) and \(\phi\), respectively, can be found by the solution of the score equations \(s_\beta = 0_p\) and \(s_\phi = 0\).

Let \(A_\beta = -i_{\beta\beta} b_\beta\) and \(A_\phi = -i_{\phi\phi} b_\phi\), where \(b_\beta\) and \(b_\phi\) are the first terms in the expansion of the mean bias of the maximum likelihood estimator of the regression parameters \(\beta\) and dispersion \(\phi\), respectively. The results in Firth (1993) can be used to show that the solution of the adjusted score equations \[\begin{align*} s_\beta + A_\beta & = 0_p \\ s_\phi + A_\phi & = 0 \end{align*}\] results in estimators \(\tilde\beta\) and \(\tilde\phi\) with bias of smaller asymptotic order than the maximum likelihood estimator.

The results in either I. Kosmidis and Firth (2009) or Cordeiro and McCullagh (1991) can then be used to re-express the adjustments in forms that are convenient for implementation. In particular, and after some algebra the bias-reducing adjustments for generalized linear models are \[\begin{align*}
A_\beta & = X^\top W \xi \,, \\
A_\phi & = \frac{(p - 2)}{2\phi} + \frac{\sum_{i = 1}^n m_i^3
a'''_i}{2\phi^2\sum_{i = 1}^n m_i^2
a''_i}
%A_\phi & = \frac{(p - 2)\phi\sum_{i = 1}^n m_i^2
% a''(-m_i/\phi) + \sum_{i = 1}^n m_i^3
% a'''(-m_i/\phi))}{2\phi^2\sum_{i = 1}^n m_i^2
% a''(-m_i/\phi)}
\end{align*}\] where \(\xi = (\xi_1, \ldots, \xi_n)^T\) with \(\xi_i = h_id_i'/(2d_iw_i)\), \(d_i' = d^2\mu_i/d\eta_i^2\), \(a''_i = a''(-m_i/\phi)\), \(a'''_i = a'''(-m_i/\phi)\), and \(h_i\) is the “hat” value for the \(i\)th observation (see, e.g. `?hatvalues`

).

The results in Kenne Pagui, Salvan, and Sartori (2017) can be used to show that if \[\begin{align*} A_\beta & = X^\top W (\xi + X u) \\ A_\phi & = \frac{p}{2\phi}+\frac{ \sum_{i = 1}^n m_i^3 a'''_i}{6\phi^2\sum_{i = 1}^n m_i^2 a''_i} \, , \end{align*}\] then the solution of the adjusted score equations \(s_\beta + A_\beta = 0_p\) and \(s_\phi + A_\phi = 0\) results in estimators \(\tilde\beta\) and \(\tilde\phi\) with median bias of smaller asymptotic order than the maximum likelihood estimator. In the above expression, \(u = (u_1, \ldots, u_p)^\top\) with \[\begin{align*} u_j = [(X^\top W X)^{-1}]_{j}^\top X^\top \left[ \begin{array}{c} \tilde{h}_{j,1} \left\{d_1 v'_1 / (6 v_1) - d'_1/(2 d_1)\right\} \\ \vdots \\ \tilde{h}_{j,n} \left\{d_n v'_n / (6 v_n) - d'_n/(2 d_n)\right\} \end{array} \right] \end{align*}\] where \([A]_j\) denotes the \(j\)th row of matrix \(A\) as a column vector, \(v'_i = V'(\mu_i)\), and \(\tilde{h}_{j,i}\) is the \(i\)th diagonal element of \(X K_j X^T W\), with \(K_j = [(X^\top W X)^{-1}]_{j} [(X^\top W X)^{-1}]_{j}^\top / [(X^\top W X)^{-1}]_{jj}\).

The results in Ioannis Kosmidis, Kenne Pagui, and Sartori (2020) can be used to show that if \[\begin{align*} A_\beta & = X^\top W \xi \,, \\ A_\phi & = \frac{p}{2\phi}+\frac{ \sum_{i = 1}^n m_i^3 a'''_i}{6\phi^2\sum_{i = 1}^n m_i^2 a''_i} \, , \end{align*}\] then the solution of the adjusted score equations \(s_\beta + A_\beta = 0_p\) and \(s_\phi + A_\phi = 0\) results in estimators \(\tilde\beta\) with mean bias of small asymptotic order than the maximum likelihood estimator and \(\tilde\phi\) with median bias of smaller asymptotic order than the maximum likelihood estimator.

The likelihood penalized by a power of the Jeffreys prior [ |i_{}|^a |i_{}|^a a > 0 ] can be maximized by solving the adjusted score equations \(s_\beta + A_\beta = 0_p\) and \(s_\phi + A_\phi = 0\) with \[\begin{align*} A_\beta & = X^\top W \rho \,, \\ A_\phi & = -\frac{p + 4}{2\phi}+\frac{ \sum_{i = 1}^n m_i^3 a'''_i}{6\phi^2\sum_{i = 1}^n m_i^2 a''_i} \, , \end{align*}\] where \(\rho = (\rho_1, \ldots, \rho_n)^T\) with \(\rho_i = h_i \{2 d_i'/(d_i w_i) - v_i' d_i/(v_i w_i)\}\).

`brglmFit`

`brglmFit()`

implements a quasi Fisher scoring procedure for solving the adjusted score equations \(s_\beta + A_\beta = 0_p\) and \(s_\phi + A_\phi = 0\). The iteration consists of an outer loop and an inner loop that implements step-halving. The algorithm is as follows:

- \(s_\beta\), \(i_{\beta\beta}\), \(A_\beta\)
- \(s_\phi\), \(i_{\phi\phi}\), \(A_\phi\)
- Starting values \(\beta^{(0)}\) and \(\phi^{(0)}\)
- \(\epsilon > 0\): tolerance for the \(L1\) norm of the direction before reporting convergence
- \(M\): maximum number of halving steps that can be taken

- \(\tilde\beta\), \(\tilde\phi\)

*Initialize outer loop*

\(k \leftarrow 0\)

\(\upsilon_\beta^{(0)} \leftarrow \left\{i_{\beta\beta}\left(\beta^{(0)}, \phi^{(0)}\right)\right\}^{-1} \left\{s_\beta\left(\beta^{(0)}, \phi^{(0)}\right) + A_\beta\left(\beta^{(0)}, \phi^{(0)}\right)\right\}\)

\(\upsilon_\phi^{(0)} \leftarrow \left\{i_{\phi\phi}\left(\beta^{(0)}, \phi^{(0)}\right)\right\}^{-1} \left\{s_\phi\left(\beta^{(0)}, \phi^{(0)}\right) + A_\phi\left(\beta^{(0)}, \phi^{(0)}\right)\right\}\)

*Initialize inner loop*

\(m \leftarrow 0\)

\(b^{(m)} \leftarrow \beta^{(k)}\)

\(f^{(m)} \leftarrow \phi^{(k)}\)

\(v_\beta^{(m)} \leftarrow \upsilon_\beta^{(k)}\)

\(v_\phi^{(m)} \leftarrow \upsilon_\phi^{(k)}\)

\(d \leftarrow \left|v_\beta^{(m)}\right|_1 + \left|v_\phi^{(m)}\right|\)

*Update parameters*

\(b^{(m + 1)} \leftarrow b^{(m)} + 2^{-m} v_\beta^{(m)}\)

\(f^{(m + 1)} \leftarrow f^{(m)} + 2^{-m} v_\phi^{(m)}\)

*Update direction*

\(v_\beta^{(m + 1)} \leftarrow \left\{i_{\beta\beta}\left(b^{(m + 1)}, f^{(m + 1)}\right)\right\}^{-1} \left\{s_\beta\left(b^{(m + 1)}, f^{(m + 1)}\right) + A_\beta\left(b^{(m + 1)}, f^{(m + 1)}\right)\right\}\)

\(v_\phi^{(m + 1)} \leftarrow \left\{i_{\phi\phi}\left(b^{(m + 1)}, f^{(m + 1)}\right)\right\}^{-1} \left\{s_\phi\left(b^{(m + 1)}, f^{(m + 1)}\right) + A_\phi\left(b^{(m + 1)}, f^{(m + 1)}\right)\right\}\)

*Continue or break halving within inner loop*

if \(m + 1 < M\) and \(\left|v_\beta^{(m + 1)}\right|_1 + \left|v_\phi^{(m + 1)}\right| > d\)

14.1. \(m \leftarrow m + 1\)

14.2. GO TO 10

else

15.1. \(\beta^{(k + 1)} \leftarrow b^{(m + 1)}\)

15.2. \(\phi^{(k + 1)} \leftarrow f^{(m + 1)}\)

15.3. \(\upsilon_\beta^{(k + 1)} \leftarrow v_b^{(m + 1)}\)

15.4. \(\upsilon_\phi^{(k + 1)} \leftarrow v_f^{(m + 1)}\)

*Continue or break outer loop*

if \(k + 1 < K\) and \(\left|\upsilon_\beta^{(k + 1)}\right|_1 + \left|\upsilon_\phi^{(k + 1)}\right| > \epsilon\)

16.1 \(k \leftarrow k + 1\)

16.2. GO TO 4

else

17.1. \(\tilde\beta \leftarrow \beta^{(k + 1)}\)

17.2. \(\tilde\phi \leftarrow \phi^{(k + 1)}\)

17.3. STOP

For \(K = M = 1\), \(\beta^{(0)} = \hat\beta\) and \(\phi^{(0)} = \hat\phi\), the above iteration computes the bias-corrected estimates proposed in Cordeiro and McCullagh (1991). This is achieved when the

`brglmFit()`

function is called with`type = "correction"`

(see`?brglmFit`

).The mean-bias reducing adjusted score functions are solved when the

`brglmFit()`

function is called with`type = "AS_mean"`

, and the median-bias reducing adjusted score functions with`type = "AS_median"`

(see`?brglmFit`

). Estimation using mixed adjustments is through`type = "AS_mixed"`

.`type = "MPL_Jeffreys"`

does maximum penalized likelihood with a power of the Jeffreys prior as penalty.The steps where \(\phi\) and the \(\phi\) direction are updated are ignored for generalized linear models with known dispersion parameter, like in models with binomial and Poisson responses. Also, in that case, \(v_\phi^{(.)}\) and \(\upsilon_\phi^{(.)}\) in steps 9, 14 and 16 are set to zero.

The implementation of the adjusted score functions requires ready implementations of \(d^2\mu_i/d\eta_i^2\), \(a'(.)\), \(a''(.)\) and \(a'''(.)\). The

**enrichwith**R package is used internally to enrich the base`family`

and`link-glm`

objects with implementations of those functions (see`?enrich.family`

and`?enrich.link-glm`

).The above iteration can be used to implement a variety of additive adjustments to the score function, by supplying the algorithm with appropriate adjustment functions \(A_\beta\) and \(A_\phi\)

The first version of the vignette has been written by Ioannis Kosmidis. Eugene Clovis Kenne Pagui and Nicola Sartori contributed the first version of the section “Median bias-reducing adjusted score functions,” and Ioannis Kosmidis brought the expressions for the median bias-reducing adjustments in the reduced form that is shown above and is implemented in `brglmFit()`

.

Ioannis Kosmidis, Kenne Pagui, and Sartori (2020) provides more details about mean and median bias reduction in generalized linear models.

If you found this vignette or **brglm2**, in general, useful, please consider citing **brglm2** and the associated paper. You can find information on how to do this by typing `citation("brglm2")`

.

Albert, A., and J. A. Anderson. 1984. “On the Existence of Maximum Likelihood Estimates in Logistic Regression Models.” *Biometrika* 71 (1): 1–10.

Cordeiro, G. M., and P. McCullagh. 1991. “Bias Correction in Generalized Linear Models.” *Journal of the Royal Statistical Society, Series B: Methodological* 53 (3): 629–43.

Firth, D. 1993. “Bias Reduction of Maximum Likelihood Estimates.” *Biometrika* 80 (1): 27–38.

Kenne Pagui, E. C., A. Salvan, and N. Sartori. 2017. “Median Bias Reduction of Maximum Likelihood Estimates.” *Biometrika* 104: 923–38. https://doi.org/10.1093/biomet/asx046.

Kosmidis, I. 2014. “Bias in Parametric Estimation: Reduction and Useful Side-Effects.” *Wiley Interdisciplinary Reviews: Computational Statistics* 6 (3): 185–96. https://doi.org/10.1002/wics.1296.

Kosmidis, I., and D. Firth. 2009. “Bias Reduction in Exponential Family Nonlinear Models.” *Biometrika* 96 (4): 793–804. https://doi.org/10.1093/biomet/asp055.

———. 2010. “A Generic Algorithm for Reducing Bias in Parametric Estimation.” *Electronic Journal of Statistics* 4: 1097–1112. https://doi.org/10.1214/10-EJS579.

Kosmidis, Ioannis, Euloge Clovis Kenne Pagui, and Nicola Sartori. 2020. “Mean and Median Bias Reduction in Generalized Linear Models.” *Statistics and Computing* 30: 43–59. https://doi.org/10.1007/s11222-019-09860-6.