2. Bivariate Shock Models within Competing Risks

In this section we describe and study the bivariate shock model driven by (generic) counting processes within the competing risks framework. In particular, we provide sufficient conditions for which the failure time and the cause of failure are independent.

Let T be a random lifetime with an absolutely continuous distribution that describes the failure time of an item (system or a living organism) having survival function (SF) defined as $\overline{F}_T(t)=\mathbb{P}(T>t)$ for $t\ge 0$ . We assume that the item is initially active, i.e. $\overline{F}_T(0)=1$ . Moreover, it fails in a certain instant, so that $\lim_{t\to +\infty}\overline{F}_T(t)=0$ . Let $\delta=i$ if the failure occurs due to a shock of type i for $i=1,2$ . We denote by $N_i(t)$ , for $i=1,2$ , the counting process representing the number of shocks of type i occurring in [0, t].

We can partition the state space $\mathbb{N}^2_0$ of $(N_1(t),N_2(t))$ into nonempty subsets

(1) \begin{equation}S_k\;:\!=\;\{(x_1,x_2)\in \mathbb{N}^2_0\;:\;\Psi (x_1,x_2)=k\}, \qquad k\in\mathbb{N}_0,\end{equation}

called failure sets, where $\Psi\;:\;\mathbb{N}^2_0\longrightarrow\mathbb{N}$ is a given suitable function that creates a partition of the state space. We assume that $S_0$ includes the numbers of nonfatal shocks; namely, if $(N_1(t),N_2(t))\in S_0$ then the shocks that arrived up to time t do not cause a failure.

We denote by M the integer-valued random variable that represents the index of which failure set $S_k$ , $k\in\mathbb{N}$ , contains the numbers of fatal shocks, independent of $(N_1(t),N_2(t))$ . In other words, $M=k$ if and only if the system fails at the first instant $t>0$ in which $(N_1(t),N_2(t))\in S_k$ . For $k\in\mathbb{N}$ the risky sets are defined as

(2) \begin{equation}\begin{array}{l}R_k^{(1)}\;:\!=\; \{(x_1,x_2)\in \mathbb{N}^2_0\setminus S_k\;:\;(x_1+1,x_2)\in S_k\}, \\[5pt] R_k^{(2)}\;:\!=\;\{(x_1,x_2)\in \mathbb{N}^2_0\setminus S_k\;:\;(x_1,x_2+1)\in S_k\},\end{array}\end{equation}

where $R_k^{(i)}$ , $i=1,2$ , contains all states of $\mathbb{N}^2_0$ leading to a failure at time t at the next occurrence of a shock of type i, given that $M=k$ . Note that, from (2), the failures are due to a single cause. In addition, we remark that in general $R_k^{(i)}$ is different from $S_{k-1}$ . Under these assumptions, the failure time can be denoted as

$$T=\inf\{t\ge 0 \;:\; \Psi\left(N_1(t),N_2(t)\right)=M\}.$$

The probability density function (PDF) of T is expressed as

$$f_T(t)=f_1(t)+f_2(t),\qquad t\geq 0,$$

where $f_i$ , for $i=1,2$ , is the failure density (FD), also named subdensity function, defined by

(3) \begin{equation}f_i(t)=\frac{\textrm{d}}{\textrm{d}t}\mathbb{P}\{T\leq t,\,\delta=i\}, \qquad t\geq 0.\end{equation}

Note that $f_i$ , for $i=1,2$ , is not a proper PDF. Indeed, since $\delta=i$ , when the failure occurs due to a shock of type i, we have

(4) \begin{equation}\mathbb{P}(\delta=i)=\int_0^{+\infty} f_i(t)\,\textrm{d}t,\qquad i=1,2.\end{equation}

From (3) and (4), we can obtain the moments of the failure time conditioned on the cause of failure for $s\ge 1$ as

(5) \begin{equation}\mathbb{E}(T^s\,|\,\delta=i)=\frac{1}{\mathbb{P}(\delta=i)}\int_{0}^{+\infty} t^s\,f_i(t)\,\textrm{d}t, \qquad i=1,2.\end{equation}

To express the FDs $f_i$ for $i=1,2$ in terms of the joint probability distribution of $(N_1(t),N_2(t))$ , we introduce the following hazard rates:

(6) \begin{equation}\begin{array}{l}r_1(x_1,x_2;\; t)=\displaystyle\lim_{\tau\to 0^+} \frac{1}{\tau}\mathbb{P}[N_1(t+\tau)= x_1+1, N_2(t+\tau)= x_2| N_1(t)=x_1, N_2(t)=x_2], \\[9pt] r_2(x_1,x_2;\; t)=\displaystyle\lim_{\tau\to 0^+} \frac{1}{\tau}\mathbb{P}[N_1(t+\tau)= x_1, N_2(t+\tau)= x_2+1| N_1(t)=x_1, N_2(t)=x_2],\end{array}\end{equation}

with $(x_1,x_2)\in\mathbb{N}^2_0$ and $t\geq 0$ . Hence, given that $x_1$ shocks of type 1 and $x_2$ shocks of type 2 occurred in [0, t], $r_i(x_1,x_2;\; t)$ gives the intensity of the occurrence of a shock of type i immediately after t, with $i=1,2$ . Note that $r_1(x_1,x_2;\; t)+r_2(x_1,x_2;\; t)$ is the hazard rate of a shock of any type.

From the assumptions stated above, the item fails at time t due to a shock of type i if the two-dimensional counting process $(N_1(t),N_2(t))$ takes values in $R_k^{(i)}$ at time t and a shock of type i occurs immediately after. Hence, conditioning on M and recalling (2) and (6), for $i=1,2$ , we have

(7) \begin{equation}f_i(t)=\sum_{k=1}^{+\infty} p_M(k) \sum_{(x_1,x_2)\in R_k^{(i)}}\mathbb{P}[N_1(t)=x_1,N_2(t)=x_2]\,r_i(x_1,x_2;\;t),\qquad t\ge 0,\end{equation}

where $p_M(k)=\mathbb{P}(M=k)$ , for $k\in\mathbb{N}$ , denotes the probability distribution of M. Therefore, by using (7), we can express (4) and (5) in terms of the joint probability distribution of $(N_1(t),N_2(t))$ .

We recall that a multivariate counting process $\{\textbf{N}(t), t\in\mathbb{R}^+_0\}$ has the multinomial property if the identity

(8) \begin{equation}\begin{aligned}&\mathbb{P}\left[\bigcap_{j=1}^m \left\{\textbf{N}(t_j) -\textbf{N}(t_{j-1})=\textbf{n}_j \right\}\right]\\[5pt] &=\left(\prod_{i=1}^\ell\frac{\left( \sum_{j=1}^m n_j^{(i)} \right)!}{\prod_{j=1}^m n_j^{(i)}!}\prod_{j=1}^m\left(\frac{t_j - t_{j-1}}{t_m}\right)^{n_j^{(i)}}\right)\mathbb{P}\left[\left\{\textbf{N}(t_m)=\sum_{j=1}^m \textbf{n}_j\right\}\right] \end{aligned}\end{equation}

holds for all $m \in \mathbb{N}$ and $t_0, t_1, \ldots, t_m\in\mathbb{R}^+_0$ with $0 = t_0 < t_1 < \cdots < t_m$ , and for all $\textbf{n}_j \in \mathbb{N}_0^\ell$ , $j \in \{1, \ldots, m\}$ ; see Section 2.2 in [Reference Zocher38]. Hereafter, we characterize the expression for the FDs given in (7) by assuming that the corresponding bivariate counting process has the multinomial property.

Proposition 1. If $\textbf{N}(t)=(N_1(t),N_2(t))$ has the multinomial property, then

(9) \begin{equation}f_i(t)=\sum_{k=1}^{+\infty}p_M(k)\sum_{(x_1,x_2)\in R^{(i)}_k}(x_i+1)t^{x_1+x_2}\lim_{\tau\to 0^+}\frac{\mathbb{P}[N_i(t+\tau)=x_i+1,N_j(t+\tau)=x_j]}{(t+\tau)^{x_1+x_2+1}}\end{equation}

for $i=1,2$ , $j=3-i$ , and $t\ge 0$ .

Proof. We prove the result for $i=1$ , since in a similar way it follows for $i=2$ . From (6), we have

$$\displaystyle\begin{aligned}&r_1(x_1,x_2;\;t)\\[5pt] &=\lim_{\tau\to 0^+}\frac{1}{\tau}\frac{\mathbb{P}\left[N_1(t+\tau)=x_1+1, N_2(t+\tau)=x_2, N_1(t)=x_1, N_2(t)=x_2\right]}{\mathbb{P}\left[\left(N_1(t),N_2(t)\right)=(x_1,x_2)\right]}\\[5pt] &=\lim_{\tau\to 0^+}\frac{1}{\tau}\frac{\mathbb{P}\left[\{\left(N_1(t),N_2(t)\right)=(x_1,x_2)\}\cap\{\left(N_1(t+\tau)-N_1(t),N_2(t+\tau)-N_2(t)\right)=(1,0)\}\right]}{\mathbb{P}\left[\left(N_1(t),N_2(t)\right)=(x_1,x_2)\right]}\\[5pt] &=(x_1+1)t^{x_1+x_2}\lim_{\tau\to 0^+}\frac{\mathbb{P}\left[\left(N_1(t+\tau),N_2(t+\tau)\right)=(x_1+1,x_2)\right]}{(t+\tau)^{x_1+x_2+1}\,\mathbb{P}\left[\left(N_1(t),N_2(t)\right)=(x_1,x_2)\right]},\end{aligned}$$

having used in the last equality the multinomial property given in (8) for $\ell=m=2$ . Hence, the result follows by applying the latter expression in (7).

Since $\{T>t\}=\{\Psi\left(N_1(t),N_2(t)\right)<M\}$ for all $t\ge 0$ , by conditioning on $(N_1(t),N_2(t))$ , from (1) we obtain

(10) \begin{equation}\overline F_T(t)=\sum_{k=0}^{+\infty} \overline F_M(k) \sum_{(x_1,x_2)\in S_k}\mathbb{P}[N_1(t)=x_1,N_2(t)=x_2], \qquad t\geq 0,\end{equation}

where $\overline F_M(k)$ , for $k\in\mathbb{N}_0$ , is the SF of M such that $\overline F_M(0)=1$ .

Hereafter we provide sufficient conditions for which the random failure time T and the cause of failure $\delta$ are independent. We refer to [Reference Kella26] for a characterization of the independence between time and cause of failure for a general item.

Theorem 1. In (1), let us assume $\Psi(x_1, x_2)=\Psi(x_2, x_1)$ for all $(x_1,x_2)\in \mathbb{N}^2_0$ . If

(11) \begin{equation}(N_1(t),N_2(t))=_{\mathrm{st}}(N_2(t),N_1(t))\quad \mbox{for all} \ t\ge 0,\end{equation}

then T and $\delta$ are independent, where $=_{\mathrm{st}}$ means equality in law.

Proof. Under the stated hypothesis on $\Psi$ , from (2) it is easy to see that $(x_1,x_2)\in R_k^{(1)}$ if and only if $(x_2,x_1)\in R_k^{(2)}$ , for any $(x_1,x_2)\in \mathbb{N}^2_0$ . Therefore, from (7), by using (11), it follows that $f_1(t)=f_2(t)$ for all $t\ge 0$ . Hence, from (4) we have $\mathbb{P}(\delta=1)=\mathbb{P}(\delta=2)=1/2$ , which allows us to obtain for $i=1,2$

$$\mathbb{P}(\delta=i)f_T(t)=f_i(t),\qquad t\ge 0,$$

and the proof is completed.

From this point on we assume that shocks are driven by homogeneous geometric counting processes.

3. Results under the Geometric Counting Process

In this section we assume that the two kinds of shock affect the system according to homogeneous geometric counting processes $N_1(t)$ and $N_2(t)$ , respectively. The definition of the geometric counting process can be stated as follows.

Definition 1. Let $\{N^{(\alpha)}(t), t\in\mathbb{R}^+_0\}$ be a Poisson process with intensity $\alpha$ . Let U be an exponential distribution with mean $\lambda\in\mathbb{R}^+$ . The process $\{N(t),t\in\mathbb{R}^+_0\}$ having marginal distributions for $x\in\mathbb{N}_0$ given by

$$\mathbb{P}\left[N(t)=x\right]=\int_{0}^{+\infty}\mathbb{P}\big[N^{(\alpha)}(t)=x\big]\textrm{d}U(\alpha),\qquad t\in\mathbb{R}^+_0,$$

is named geometric counting process with intensity $\lambda$ .

We now extend the geometric counting process to the bivariate case.

Definition 2. Let $\{N_1^{(\alpha)}(t), t\in\mathbb{R}^+_0\}$ and $\{N_2^{(\alpha)}(t), t\in\mathbb{R}^+_0\}$ be independent Poisson processes, both having intensity $\alpha\in\mathbb{R}^+$ . Let U be the distribution of $\max\{T_1,T_2\}$ , where $T_i$ has an exponential distribution with mean $\frac1{\lambda_i}\in\mathbb{R}^+$ for $i=1,2$ . The bivariate process $\boldsymbol{N}(t)=(N_1(t),N_2(t))$ , for $t\in\mathbb{R}^+_0$ , having joint distribution for $x_1,x_2\in \mathbb{N}_0$ given by

(12) \begin{equation}\mathbb{P}[N_1(t)=x_1,N_2(t)=x_2]=\int_{\mathbb{R}^+}\mathbb{P}\big[N_1^{(\alpha)}(t)=x_1,N_2^{(\alpha)}(t)=x_2\big]\textrm{d}U(\alpha),\qquad t\in\mathbb{R}^+_0,\end{equation}

is named bivariate geometric counting process, where the intensity of $N_i$ is 1/ $\lambda_i$ , for $i=1,2$ .

For the next result we recall that the gamma function is defined as

(13) \begin{equation}\Gamma(z)\;:\!=\;\int_0^{+\infty}t^{z-1}\mathrm{e}^{-t}\textrm{d}t,\end{equation}

with $Re(z)>0$ (see, for instance, the equation 6.1.1 in [Reference Abramowitz and Stegun1]).

Proposition 2. Under the assumptions of Definition 2, if $T_1$ and $T_2$ are independent, then

(14) \begin{equation}\begin{aligned}&\mathbb{P}\left[N_1(t)=x_1,N_2(t)=x_2\right]=\left(\substack{x_1+x_2\\ x_1}\right)t^{x_1+x_2}\\[5pt] &\quad\times\left[\frac{\lambda_1}{(2t+\lambda_1)^{x_1+x_2+1}}+\frac{\lambda_2}{(2t+\lambda_2)^{x_1+x_2+1}}-\frac{\lambda_1+\lambda_2}{(2t+\lambda_1+\lambda_2)^{x_1+x_2+1}}\right] \end{aligned}\end{equation}

for $x_1,x_2\in \mathbb{N}_0$ .

Proof. Under the stated hypothesis, from (12) it holds that

$$\begin{aligned}&\mathbb{P}\left[N_1(t)=x_1,N_2(t)=x_2\right]\\[5pt] &\quad =\int_{\mathbb{R}^+}\mathrm{e}^{-2\alpha t}\frac{(\alpha t)^{x_1+x_2}}{x_1!x_2!}\left[\lambda_1\mathrm{e}^{-\lambda_1\alpha}(1-e^{-\lambda_2\alpha})+\lambda_2\mathrm{e}^{-\lambda_2\alpha}(1-\mathrm{e}^{-\lambda_1\alpha})\right]\textrm{ d}\alpha\\[5pt] &\quad =\frac{t^{x_1+x_2}\left[h(x_1+x_2;\;\lambda_1)+h(x_1+x_2;\;\lambda_2)- h(x_1+x_2;\;\lambda_1+\lambda_2)\right]}{x_1!x_2!}, \end{aligned}$$

where

$$h(x;\;\lambda)\;:\!=\;\int_0^{+\infty} \lambda\alpha^{x} \mathrm{e}^{-\alpha(2t+\lambda)}\textrm{d}\alpha=\frac{\lambda\Gamma(x+1)}{(2t+\lambda)^{x+1}},$$

having set $u=\alpha(2t+\lambda)$ in the last equality and by recalling (13). Then, since $\Gamma(x+1)=x!$ , for $x\in\mathbb{N}_0$ , the thesis follows by straightforward calculations.

Note that, under the assumptions of Proposition 2, (11) holds. Moreover, (14) can be rewritten as

$$\mathbb{P}\left[N_1(t)=x_1,N_2(t)=x_2\right]=\left(\begin{array}{c}x_1+x_2 \\[2pt] x_1\end{array}\right)\frac{1}{2^{x_1+x_2}}\mathbb{P}\left[\tilde{N}^{(2\max\{T_1,T_2\})}(t)=x_1+x_2\right]\!,$$

where $T_1$ and $T_2$ are independent exponential random variables with means $1/\lambda_1$ and $1/\lambda_2$ , respectively, and $\tilde{N}^{(\alpha)}(t)$ denotes a Poisson process with intensity $\alpha\in\mathbb{R}^+$ and that is independent of $T_1$ and $T_2$ .

As a consequence of Remark 2 and Proposition 2, from (9) it follows that

(15) \begin{equation}\begin{aligned}f_i(t)&=\sum_{k=1}^{+\infty}p_M(k)\sum_{(x_1,x_2)\in R^{(i)}_k}(x_1+x_2+1)\left(\begin{array}{c}x_1+x_2 \\[2pt] x_1\end{array}\right)t^{x_1+x_2}\\[5pt] &\quad\times \left[\frac{\lambda_1}{(2t+\lambda_1)^{x_1+x_2+2}}+\frac{\lambda_2}{(2t+\lambda_2)^{x_1+x_2+2}}-\frac{\lambda_1+\lambda_2}{(2t+\lambda_1+\lambda_2)^{x_1+x_2+2}}\right] \end{aligned}\end{equation}

for $i=1,2$ and $t\ge 0$ . Moreover, from (10) and (14), the SF of T becomes

(16) \begin{equation}\begin{aligned}\overline{F}_T(t)=&\sum_{k=0}^{+\infty}\overline{F}_M(k)\sum_{(x_1,x_2)\in S_k}\left(\begin{array}{c}x_1+x_2 \\[2pt] x_1\end{array}\right)t^{x_1+x_2}\\[5pt] &\times\left[\frac{\lambda_1}{(2t+\lambda_1)^{x_1+x_2+1}}+\frac{\lambda_2}{(2t+\lambda_2)^{x_1+x_2+1}}-\frac{\lambda_1+\lambda_2}{(2t+\lambda_1+\lambda_2)^{x_1+x_2+1}}\right] \end{aligned}\end{equation}

for $t\ge 0$ .

We now specify the expression for the probability of the cause of failure given in (4) under the hypothesis that shocks are governed by the bivariate geometric counting process having probability law given in (14). For this aim, we recall that the well-known beta function can be expressed as

(17) \begin{equation}B(z,w)=\int_0^{+\infty}\frac{t^{z-1}}{(t+1)^{z+w}}\textrm{d}t,\end{equation}

which converges if $\mathrm{Re}(z)>0$ and $\mathrm{Re}(w)>0$ (see, for instance, the equation 6.2.1 in [Reference Abramowitz and Stegun1]).

Proposition 3. Under the above assumptions, for $i=1,2$ it follows that

(18) \begin{equation}\mathbb{P}(\delta=i)=\sum_{k=1}^{+\infty}p_M(k)\sum_{(x_1,x_2)\in R_k^{(i)}}\left(\begin{array}{c}x_1+x_2 \\[2pt] x_1\end{array}\right)2^{-x_1-x_2-1}.\end{equation}

Proof. Without loss of generality we prove the result for $i=1$ . From (4) and (15) we have

$$\mathbb{P}(\delta=1)=\sum_{k=1}^{+\infty}p_M(k)\sum_{(x_1,x_2)\in R_k^{(1)}}(x_1+x_2+1)\left(\begin{array}{c}x_1+x_2 \\[2pt] x_1\end{array}\right)\varphi(x_1+x_2), $$

where

$$\varphi(x)\;:\!=\;\int_0^{+\infty}\frac{\lambda t^{x}}{(2t+\lambda)^{x+2}}\textrm{d}t=2^{-x-1}B(x+1,1),$$

having set $y=2t/\lambda$ in the integral and by recalling (17). Then, since $B(x,1)=1/x$ , (18) follows by straightforward calculations.

Note that, from (18), the probability of the cause of failure does not depend on the respective intensities $\lambda_1$ and $\lambda_2$ of the geometric counting processes.

Hereafter we prove that, in the proposed competing risks framework, the conditioned moments of the failure time T given in (5) diverge for all $s\ge 1$ .

Proposition 4. Under the above assumptions, for $i=1,2$ it follows that

$$\mathbb{E}(T^s|\delta=i)=+\infty,\quad s\ge 1.$$

Proof. For $i=1$ , from (5) and (15) we have

\begin{equation*}\begin{aligned}&\mathbb{P}(\delta=1)\mathbb{E}(T^s|\delta=1)\\[5pt] &\quad =\sum_{k=1}^{+\infty}p_M(k)\sum_{(x_1,x_2)\in R_k^{(1)}}(x_1+x_2+1)\left(\substack{x_1+x_2\\ x_1}\right)\\[5pt] &\qquad\times\left[g(x_1+x_2;\;s;\;\lambda_1)+g(x_1+x_2;\;s;\;\lambda_2)-g(x_1+x_2;\;s;\;\lambda_1+\lambda_2)\right]\\[5pt] &\quad =\sum_{k=1}^{+\infty}p_M(k)\sum_{(x_1,x_2)\in R_k^{(1)}}(x_1+x_2+1)\left(\substack{x_1+x_2\\ x_1}\right)\\[5pt] &\qquad\times\frac{[\lambda_1^s+\lambda_2^s-(\lambda_1+\lambda_2)^s]B(x_1+x_2+s+1,1-s)}{2^{x_1+x_2+s+1}}, \end{aligned}\end{equation*}

where

$$g(x;\;s;\;\lambda)\;:\!=\;\int_0^{+\infty}\frac{\lambda t^{x+s}}{(2t+\lambda)^{x+2}}\textrm{d}t=\frac{\lambda^s B(x+s+1,1-s)}{2^{x+s+1}},$$

having set $y=2t/\lambda$ in the integral and by recalling (17). We can do the same similarly for $i=2$ . Hence, from (5), the thesis follows since $B(x_1+x_2+s+1,1-s)=+\infty$ for all $s\ge 1$ .

Clearly, from the above results, the nature of the shock model is completely specified when the structures of the failure sets and of the risky sets are fixed. Hence, in the rest of this article, we consider a special case for the failure scheme. Specifically, by recalling (1), we assume that $\Psi(x_1,x_2)=x_1+x_2$ .

4. Failures due to the Sum of Shocks

In this section we assume that the system fails when the sum of shocks of type 1 and of type 2 reaches a random threshold that takes values in $\mathbb{N}$ , keeping in mind that shocks are driven by the bivariate geometric counting process having probability law given in (14). Hence, from (1) we have

(19) \begin{equation}S_k= \{(x_1,x_2)\in \mathbb{N}^2_0\;:\; x_1+x_2=k\},\qquad k\in\mathbb{N}_0,\end{equation}

and

(20) \begin{equation}R_k^{(1)}=R_k^{(2)}=S_{k-1}, \qquad k\in\mathbb{N};\end{equation}

see Figure 1. Note that $S_0=\{(0,0)\}$ , which corresponds to no shocks of any kind having occurred during the system’s working period.

Figure 1.

Failure scheme due to the sum of shocks.

In reliability theory, such a model may be appropriate to describe, for instance, the failure of systems made of units connected in series, as described below.

Remark 4. Let $X_1$ and $X_2$ describe the respective random failure time of two components in a series system having random lifetime denoted by $T=\min\{X_1,X_2\}$ . We assume that the component with lifetime $X_i$ is subject to a shock of type i for $i=1,2$ . If $\delta$ denotes the related cause of failure, then

$$\mathbb{P}\left(\delta=i\right)=\mathbb{P}\left(X_i=T\right),\qquad i=1,2.$$

An example of this kind of system is given in Figure 2. For more details about system reliability theory, we refer the reader to [Reference Navarro30].

Hereafter, from (15), we specify the expression for the PDF of T according to the failure scheme of the sum.

Figure 2.

Example of a series system made of two units.

Proposition 5. Under the assumptions leading to (19) and (20), it follows that

(21) \begin{equation}f_T(t)=\sum_{k=1}^{+\infty}2k(2t)^{k-1}p_M(k)\left[\frac{\lambda_1}{(2t+\lambda_1)^{k+1}}+\frac{\lambda_2}{(2t+\lambda_2)^{k+1}}-\frac{\lambda_1+\lambda_2}{(2t+\lambda_1+\lambda_2)^{k+1}}\right]\end{equation}

for $t\ge 0$ .

Proof. By using (20) in (15), it holds that

$$f_1(t)=\sum_{k=1}^{+\infty}kp_M(k)t^{k-1}\left[\frac{\lambda_1}{(2t+\lambda_1)^{k+1}}+\frac{\lambda_2}{(2t+\lambda_2)^{k+1}}-\frac{\lambda_1+\lambda_2}{(2t+\lambda_1+\lambda_2)^{k+1}}\right]\left[\sum_{x_1=0}^{k-1}\left(\begin{array}{c}k-1 \\[2pt] x_1\end{array}\right)\right].$$

Then the thesis follows from

$$\sum_{x_1=0}^{k-1}\left(\begin{array}{c}k-1 \\[2pt] x_1\end{array}\right)=2^{k-1}$$

and by taking into account that, from Remark 3, it holds that $f_T=2f_1$ .

A similar result can be obtained for the SF of T given in (16), the proof of which is omitted for brevity.

Proposition 6. Under the assumptions leading to (19), it holds that

(22) \begin{equation}\overline{F}_T(t)=\displaystyle\sum_{k=0}^{+\infty}\overline{F}_M(k)(2t)^k\left[\frac{\lambda_1}{(2t+\lambda_1)^{k+1}}+\frac{\lambda_2}{(2t+\lambda_2)^{k+1}}-\frac{\lambda_1+\lambda_2}{(2t+\lambda_1+\lambda_2)^{k+1}}\right]\end{equation}

for $t\ge 0$ .

In the following we provide some examples of applications of Propositions 5 and 6 according to different choices for the random threshold M. We also consider the hazard (or failure) rate function of a random lifetime T, which is defined by

(23) \begin{equation}h_T(t)\;:\!=\;\lim_{\varepsilon \to 0^+} \frac{\mathbb{P}(t < T < t + \varepsilon \mid T > t)}{\varepsilon}=\frac{f_T(t)}{\overline{F}_T(t)}\end{equation}

for all t such that $\overline{F}_T(t)>0$ . Such an ageing function has an interpretation different from that of the hazard rates introduced in (6), since it represents the average probability of failure in the interval $[t, t + \varepsilon]$ when $\varepsilon\rightarrow0^+$ for an item that is working at time t (see Section 2.2 in [Reference Navarro30]).

Example 1. Let M be a geometric distributed random variable with parameter $p\in(0,1)$ , i.e. $p_M(k)=p(1-p)^{k-1}$ for $k\in\mathbb{N}$ . From (21), by applying the arithmetic–geometric series

$$\sum_{k=0}^{+\infty} (a + kr) y^k = \frac{a}{1 - y} + \frac{ry}{(1 - y)^2}$$

for $|y|<1$ , with a few calculations it follows that

(24) \begin{equation}f_T(t)=2p\left[\frac{\lambda_1}{(\lambda_1+2pt)^2}+\frac{\lambda_2}{(\lambda_2+2pt)^2}-\frac{\lambda_1+\lambda_2}{(\lambda_1+\lambda_2+2pt)^2}\right],\qquad t\ge 0.\end{equation}

Similarly, from (22), we have

(25) \begin{equation}\overline{F}_T(t)=\frac{\lambda_1}{\lambda_1+2pt}+\frac{\lambda_2}{\lambda_2+2pt}-\frac{\lambda_1+\lambda_2}{\lambda_1+\lambda_2+2pt},\qquad t\ge 0,\end{equation}

which is a generalized mixture between three Pareto type-II (Lomax) distributions, having the same unitary shape parameter and scale parameters $2p/\lambda_1, 2p/\lambda_2$ , and $2p/(\lambda_1+\lambda_2)$ , respectively. We recall that the generalized mixtures are defined as the classic ones but they might contain negative coefficients, as in (25). Moreover, from (23), it holds that

(26) \begin{equation}h_T(t)=\frac{2p}{2pt + \lambda_1} + \frac{2p}{2pt + \lambda_2} + \frac{2p}{2pt + \lambda_1 + \lambda_2} - \frac{4p}{4pt + \lambda_1 + \lambda_2},\qquad t\ge 0.\end{equation}

In Figure 3 we plot the PDF $f_T$ given in (24) on the left-hand side, the SF $\overline{F}_T$ given in (25) in the middle, and the hazard rate function $h_T(t)$ given in (26) on the right-hand side for suitable choices of the parameters $\lambda_1,\lambda_2>0$ and $p\in(0,1)$ .

Note that, from Example 1, the random thresholds considered in Figure 3 are ordered in the usual stochastic sense. Then, according to Remark 1, the corresponding random lifetimes are ordered in the usual stochastic sense too. Moreover, the hazard rate functions depicted on the right-hand side in Figure 3 are decreasing in $t\ge 0$ . Then the corresponding random lifetimes possess the property of decreasing failure rate (DFR); see, for instance, Section 4 in [Reference Navarro30]. In addition, we recall that the reversed hazard (or failure) rate function of a random lifetime T is defined by

(27) \begin{equation}\tilde{h}_T(t)\;:\!=\;\lim_{\varepsilon \to 0^+} \frac{\mathbb{P}(t - \varepsilon< T\le t \mid T \le t)}{\varepsilon}=\frac{f_T(t)}{1-\overline{F}_T(t)}\end{equation}

for all t such that $\overline{F}_T(t)<1$ , representing the instantaneous probability of failure at t for an item that has failed in the interval [0, t] (see Section 2.2 in [Reference Navarro30]). Hence, since the PDFs depicted on the left-hand side in Figure 3 are decreasing in $t\ge 0$ , the corresponding random lifetimes have the property of decreasing reversed failure rate (DRFR); see, for instance, Section 4 in [Reference Navarro30].

Figure 3.

Plots of $f_T$ (left) given in (24), $\overline{F}_T$ (middle) given in (25), and $h_T(t)$ (right) given in (26) when $\lambda_1=1$ and $\lambda_2=2$ for $p=0.25$ , $p=0.5$ , and $p=0.75$ (full, dashed, and dotted lines, respectively).

Note that, from (23) and (27), if T is DFR, then it is also DRFR. In general, the reverse implication does not hold. Indeed, in the next example, we consider a suitable choice for the random threshold M such that the corresponding random lifetime T is DRFR but not DFR. In this case the hazard rate function turns out to be increasing up to an instant close to 0, representing a high probability of an item’s failure when it starts working.

Example 2. Let M be uniformly distributed with $p_M(k)=1/3$ for $k=1,2,3$ . Then, from (21), it follows that

(28) \begin{equation}\begin{aligned}f_T(t)&=8 t^2 \left(\frac{\lambda_1}{(2 t + \lambda_1)^4}+ \frac{\lambda_2}{(2 t + \lambda_2)^4}- \frac{\lambda_1 + \lambda_2}{(2 t + \lambda_1 + \lambda_2)^4}\right)\\[3pt] &\quad+ \frac{8}{3} t \left(\frac{\lambda_1}{(2 t + \lambda_1)^3}+ \frac{\lambda_2}{(2 t + \lambda_2)^3}- \frac{\lambda_1 + \lambda_2}{(2 t + \lambda_1 + \lambda_2)^3}\right)\\[3pt] &\quad+ \frac{2}{3} \left(\frac{\lambda_1}{(2 t + \lambda_1)^2}+ \frac{\lambda_2}{(2 t + \lambda_2)^2}- \frac{\lambda_1 + \lambda_2}{(2 t + \lambda_1 + \lambda_2)^2}\right)\end{aligned}\end{equation}

for $t\ge 0$ , and, from (22), we get

(29) \begin{equation}\begin{aligned}\overline{F}_T(t)&=\frac{\lambda_1}{2 t + \lambda_1} + \frac{\lambda_2}{2 t + \lambda_2} - \frac{\lambda_1 + \lambda_2}{2 t + \lambda_1 + \lambda_2} \\[5pt] &\quad+ \frac{4}{3} t^2 \left(\frac{\lambda_1}{(2 t + \lambda_1)^3} + \frac{\lambda_2}{(2 t + \lambda_2)^3} - \frac{\lambda_1 + \lambda_2}{(2 t + \lambda_1 + \lambda_2)^3}\right)\\[5pt] &\quad+ \frac{4}{3} t \left(\frac{\lambda_1}{(2 t + \lambda_1)^2} + \frac{\lambda_2}{(2 t + \lambda_2)^2} - \frac{\lambda_1 + \lambda_2}{(2 t + \lambda_1 + \lambda_2)^2}\right) \end{aligned}\end{equation}

for $t\ge 0$ . In Figure 4, for $\lambda_1=5$ and $\lambda_2=2$ , we plot the PDF $f_T$ given in (28) on the left-hand side, the SF $\overline{F}_T$ given in (29) in the middle, and the corresponding hazard rate function

(30)

on the right-hand side, having used (23).

We conclude this section by providing an example in which M is a degenerate random variable, describing the case in which the sum of shocks that determines the item’s failure is fixed.

Figure 4.

Plots of $f_T$ (left) given in (28), $\overline{F}_T$ (middle) given in (29), and $h_T(t)$ (right) given in (30) when $\lambda_1=5$ and $\lambda_2=2$ .

Example 3. Let M be a degenerate random variable in $k=2$ , i.e. $p_M(2)=1$ and $p_M(k)=0$ for $k\in\mathbb{N}\setminus{\{2\}}$ . Then, from (21), we have

(31) \begin{equation}f_T(t)=\frac{8\lambda_1t}{(2t + \lambda_1)^3}+ \frac{8\lambda_2t}{(2t + \lambda_2)^3}- \frac{8(\lambda_1 + \lambda_2)t}{(2t + \lambda_1 + \lambda_2)^3},\qquad t\ge 0,\end{equation}

and from (22) it holds that

(32) \begin{equation}\overline{F}_T(t)=\frac{\lambda_1 (4t + \lambda_1)}{(2t + \lambda_1)^2}- \frac{4t^2}{(2t + \lambda_2)^2}+ \frac{4t^2}{(2t + \lambda_1 + \lambda_2)^2},\qquad t\ge 0.\end{equation}

In Figure 5, for $\lambda_1=\lambda_2=2$ , we plot the (unimodal) PDF $f_T$ given in (31) on the left-hand side, the SF $\overline{F}_T$ given in (32) in the middle, and the corresponding hazard rate function

(33) \begin{equation}h_T(t)=\frac{2t\left(7+ 3t(3+t)\right)}{(1+t)(2+t)(2+3t(2+t))},\qquad t\ge 0,\end{equation}

on the right-hand side, having used (23).

5. Concluding Remarks and Future Developments

Shock models describe how systems or living organisms fail due to random stresses over time. An item starts working and experiences random shocks, with failure that occurs after a random number of these shocks. The geometric counting process represents a relevant option in the context of counting processes in order to handle random occurrences between events that are not independent. In this article shock models driven by the bivariate geometric counting process have been studied within a competing risks framework. We assume that the mixing distribution is given by the maximum of two independent exponential distributions. In addition, the dependence between the marginal geometric counting processes comes from the hypothesis that intensities of baseline Poisson processes are shared. We have assumed that the failures are due to one of two mutually exclusive causes. In particular, failures occur at the first instant in which a random threshold M is reached by a suitable function $\Psi$ of the two type of shock, given in (1). Under these assumptions we have obtained FDs, SFs, the probability of the cause of failure, and the moments of the failure time conditioned on a specific cause. Moreover, these functions have been laid down by considering $\Psi(x_1,x_2)=x_1+x_2$ . Particular cases have been analyzed for fixed distributions of M.

Figure 5.

Plots of $f_T$ (left) given in (31), $\overline{F}_T$ (middle) given in (32), and $h_T(t)$ (right) given in (33) when $\lambda_1=\lambda_2=2$ .

Other functions $\Psi$ can be considered as long as they create certain partitions of the state space analogous to (1), such as (i) $\Psi(x_1,x_2)=\min\{x_1,x_2\}$ and (ii) $\Psi(x_1, x_2)=\max\{x_1,x_2\}$ . In addition, aiming to deal with more general settings, we can make alternative choices of counting processes that govern the shocks as well (see [Reference Cha and Finkelstein9, Reference Meoli28]). Note that each shock can be governed by a different type of counting process (for instance, one by the Poisson counting process and the other by the geometric counting process).

Further dependency structures related to the counting processes involved can be considered and described by copulas (see, for instance, [Reference Durante and Sempi18]).

Another option for future research would be to develop a comprehensive inferential framework for the proposed shock models within competing risks, including methods for parameter estimation, model validation, and statistical testing.

Finally, the proposed competing risks model can be extended to the case of an arbitrary number r of types of shock. Let $(N_1(t),\ldots,N_r(t))$ be a multidimensional counting process, where $N_i(t)$ describes the number of shocks of the ith type that occurred in [0, t] for $i=1,2,\ldots,r$ . Then the SF of T can be expressed as

$$\overline F_T(t)=\sum_{k=0}^{+\infty} p_M(k)\,\mathbb{P}\{\overline{\Psi}(N_1(t),\dots,N_r(t))<k\},\qquad t\geq 0,$$

where $\overline{\Psi}$ extends the function $\Psi$ defined in (1) to the case of r components.