这是一份非参数统计作业代写的成功案

$$
\operatorname{Pr}\left(T_{i}>t \mid T_{i} \geq t\right)=1-p_{i t} .
$$
The discrete-time survivor function can be expressed as the product of the conditional probabilities of having “survived” all previous time points or time intervals, as
$$
S_{i t}=\operatorname{Pr}\left(T_{i} \geq t\right)=\prod_{s=1}^{t-1}\left(1-p_{i s}\right) .
$$
The unconditional event probability (or probability of experiencing the event at time $t$ ) is the discrete-time analog of the continuous-time probability distribution function and may be written as
$$
\begin{aligned}
\operatorname{Pr}\left(T_{i}=t\right) &=\operatorname{Pr}\left(T_{i}=t \mid T_{i} \geq t\right) \operatorname{Pr}\left(T_{i} \geq t\right) \
&=p_{i t} S_{i t} \
&=p_{i t} \prod_{s=1}^{t-1}\left(1-p_{i s}\right)
\end{aligned}
$$

STAT 770/BIOS 805/BIOSTAT695/PSY 525/625/SOCI612/STA 4504/STA 517 COURSE NOTES ：

We assume that, conditional on $v$, the hazard rate is a product of an underlying hazard $\lambda(t)$ and the multiplicative frailty,
$$
\lambda(t \mid v)=\lambda(t) v,
$$
and that frailty (v) follows a gamma distribution,
$$
g(v)=\frac{\alpha^{\alpha} v^{\alpha-1}}{\Gamma(\alpha)} \exp (-\alpha v) \quad \text { where } \alpha>0,
$$
with mean $\mathrm{E}(v)=1$ and $\operatorname{var}(v)=1 / \alpha=\phi$.