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Let $N\ge 1$ have probability mass function $p_n$ satisfying $p_n=(a+b/n)p_{n-1}$ for $n\ge 2$. Let independent positive integer claim sizes have probability mass function $f_j$, independently of $N$. The aggregate probability mass function $g_k$ satisfies the displayed recursion, with $g_0=0$. To prove it, write $P,F,G$ for the count, severity and aggregate probability generating functions. The count recurrence gives $(1-az)P^{\prime }(z)=(a+b)P(z)+p_1$. Since $G=P\circ F$, the chain rule gives $(1-aF)G^{\prime }=((a+b)G+p_1)F^{\prime }$. Comparing coefficients of $z^{k-1}$ gives the recursion. The forcing term $p_1{f}_k$ distinguishes this algorithm from the usual Panjer recursion. For a zero-truncated Poisson distribution with parameter $\lambda$, take $a=0$, $b=\lambda$ and $p_1=\lambda /(e^{\lambda }-1)$.

For the zero-truncated Poisson distribution, $21_{\{Y\text{ even}\}}$ is the unique integrable nonrandomized unbiased estimator of $p=1-e^{-\theta }$. Indeed unbiasedness gives ${\sum }_{y\ge 1}t(y){\theta }^y/y!=e^{\theta }+e^{-\theta }-2$, and power series coefficients force the parity values. Its variance is $2p-p^2$; clipping it to $1_{\{Y\text{ even}\}}$ gives mean squared error $p/2$, strictly smaller for $0<p<1$.

Write $P(z)=\mathbb{E}[z^N]$, $F(z)=\mathbb{E}[z^{X_1}]$ and $G(z)=\mathbb{E}[z^{S_N}]$ for the three probability generating functions. Since both the claim count and every claim size are positive, $P(0)=F(0)=G(0)=0$. Conditioning on the count and using independence gives the random-sum transform identityThe crucial point is that the count recurrence begins at $n=2$. Multiplying it by $n{z}^{n-1}$ and summing, we obtainConsequently $(1-az)P^{\prime }(z)=(a+b)P(z)+p_1$. The chain rule applied to the aggregate probability generating function therefore givesThese identities hold inside the unit disk, or as identities of formal power series. Since $F(0)=0$, every coefficient of the composition depends on only finitely many count probabilities.

Compare the coefficient of $z^{k-1}$. The product $F{G}^{\prime }$ contributes ${\sum }_{j=1}^{k-1}(k-j)f_j{g}_{k-j}$, and $F^{\prime }G$ contributes ${\sum }_{j=1}^{k-1}j{f}_j{g}_{k-j}$. HenceThe required aggregate recursion for zero-truncated Panjer counts is initialized by $g_0=0$ and $g_1=p_1{f}_1$, and for every $k\ge 1$ it isThe empty sum at $k=1$ is zero. Every term on the right is known or has a smaller aggregate index. Omitting $p_1{f}_k$ would incorrectly apply the usual Panjer recursion with a zero initial value, producing zero for every aggregate probability.

For the zero-truncated Poisson distribution, $p_n/p_{n-1}=\lambda /n$ for $n\ge 2$, so $a=0$, $b=\lambda$ and $p_1=\lambda /(e^{\lambda }-1)$, with $\lambda >0$. ThusAs a direct probability generating function check, $P(z)=(e^{\lambda z}-1)/(e^{\lambda }-1)$ and therefore $G(z)=(e^{\lambda F(z)}-1)/(e^{\lambda }-1)$; differentiation reproduces this recursion.

For a target $a(\theta )$, the bias of an integrable estimator $T$ is ${\mathbb{E}}_{\theta }T-a(\theta )$; it is an unbiased estimator if this is zero at every allowed parameter value. Here $Y$ has a zero-truncated Poisson distribution, $\theta >0$, and $p=1-e^{-\theta }\in (0,1)$. Write an estimator based only on $Y$ as $t(Y)$, assuming a finite expectation for every $\theta >0$. Unbiasedness requiresAbsolute integrability at every positive parameter ensures that the power series on the left converges absolutely on every complex disc. Uniqueness of coefficients in a power series therefore gives $t(y)=0$ for odd $y$ and $t(y)=2$ for positive even $y$. Conversely these values give the displayed identity, sois the unique unbiased estimator of this form. The uniqueness claim concerns nonrandomized functions of $Y$; allowing external randomness would permit addition of independent mean-zero noise.

For this parity estimator for a zero-truncated Poisson count, usefulness depends on the loss, but under squared-error loss it performs poorly despite being an unbiased estimator. Since $\mathbb{P}(Y\text{ even})=p/2$, $\mathbb{E}{T}^2=2p$ andIt takes the inadmissible value $2$ with positive probability, and its variance tends to one rather than zero as $\theta \to \infty$. Clipping to the parameter interval gives $T_c=\min (T,1)=1_{\{Y\text{ even}\}}$. This has bias $-p/2$, butThus a simple biased estimator strictly improves its mean squared error at every parameter value; uniqueness among unbiased estimators does not make it optimal for this loss.