如果你也在 怎样代写生存模型Survival Models这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。生存模型Survival Models在许多可用于分析事件时间数据的模型中，有4个是最突出的:Kaplan Meier模型、指数模型、Weibull模型和Cox比例风险模型。
生存模型Survival Models精算师和其他应用数学家使用预测人类或其他实体(有生命或无生命)生存模式的模型，并经常使用这些模型作为相当重要的财务计算的基础。具体来说，精算师使用这些模型来计算与个人人寿保险单、养老金计划和收入损失保险相关的财务价值。人口统计学家和其他社会科学家使用生存模型对该模型适用的人口的未来构成做出预测。
statistics-lab™ 为您的留学生涯保驾护航 在代写生存模型survival model方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写生存模型survival model代写方面经验极为丰富，各种代写生存模型survival model相关的作业也就用不着说。
统计代写|生存模型代写survival model代考|Calculation of Exposure
Estimators of this form are particularly easy to apply when the basic data is represented by $\mathbf{u}{i, x}$ for person $i$ within interval $(x, x+1]$. We first note that the number of observed deaths in $(x, x+1]$ is simply the number of $\mathbf{u}{i, x}$ vectors for which $\iota_i \neq 0$. Similarly the number of observed withdrawals in $(x, x+1]$ is the number of $u{i, x}$ vectors for which $\kappa_i \neq 0$.
The exact exposure over $(x, x+1]$ contributed by person $i$ is simply
$$
(\text { Exact Exposure }){i, x}=\left[\begin{array}{c} s_i \ \iota_i \ \kappa_i \end{array}\right]-r_i, $$ where $\left[\begin{array}{l}s_i \ \iota_i \ \kappa_i\end{array}\right]$ represents the minimum of $s_i, \iota_i, \kappa_i$ that exceed zero. In other words, if $\iota_i=\kappa_i=0$, so that person $i$ neither dies nor withdraws in $(x, x+1]$, then we have (Exact Exposure) $i{i, x}=s_i-r_i$. But if person $i$ dies in $(x, x+1]$, so that $\iota_i<s_i$ and $\kappa_i=0$, then (Exact Exposure) $i_{i, x}=\iota_i-r_i$. Finally, if person $i$ withdraws in $(x, x+1)$, so that $\kappa_i<s_i$ and $\iota_i=0$, then we have (Exact Exposure $)_{i, x}=\kappa_i-r_i$.
To find the scheduled exposure for estimating the mortality probability $q_x^{\prime(d)}$ under Hoem’s moment approach, exact exposure is still used for those who withdraw and for those who neither die nor withdraw in $(x, x+1]$, but those who die are exposed to age $x+s_i$. Thus we have
$$
(\text { Scheduled Exposure })_{i, x}=\left[\begin{array}{c}
s_i \
\kappa_i
\end{array}\right]-r_i,
$$
where $\kappa_i$ is used if person $i$ withdraws in $(x, x+1]$, and $s_i$ is used otherwise.
统计代写|生存模型代写survival model代考|Grouping
In many cases an average age at event might be substituted for the exact age at event for all persons whose exact age at event falls within a certain age range. For example, consider all persons whose exact $y_i$ ‘s fall between the integers $x$ and $x+1$. We might substitute a common age $y^{\prime}$ for all such $y_i$, frequently using $y^{\prime}=x+\frac{1}{2}$ as the assumed average value of the $y_i$ ‘s. When we do this the entrants to the study have been grouped by age last birthday, since it is those with a common age last birthday $(x)$ that are being considered together.
A second type of grouping is one that is done by calendar age. Calendar age at event is defined to be the integral age $y$ obtained on the birthday in the same calendar year in which the event takes place. For example, if a person’s date of birth is September 14, 1960, and date of withdrawal is June 26,1994 , then the calendar age at withdrawal is 34 , since this person would be integral age 34 on the birthday in the calendar year of withdrawal. Calendar ages at event are easily found by
$$
C A=C Y E-C Y B,
$$
where $C Y E$ is the calendar year of the event and $C Y B$ is the calendar year of birth.
It is easy to see that a person with calendar age $w$ at event has an exact age at event that falls within $(w-1, w+1)$. If the $w^{\text {th }}$ birthday is January 1 , and the event is December 31 of the same year, then the calendar age at event is $w$ but the exact age is practically $w+1$. Conversely, if the $w^{t h}$ birthday is December 31, and the event is January 1 of the same year, then the calendar age at event is still $w$, but the exact age is virtually $w-1$.
生存模型代考
统计代写|生存模型代写survival model代考|Calculation of Exposure
当person $i$在区间$(x, x+1]$内的基本数据用$\mathbf{u}{i, x}$表示时，这种形式的估计器特别容易应用。我们首先注意到，$(x, x+1]$中观察到的死亡人数仅仅是$\iota_i \neq 0$所代表的$\mathbf{u}{i, x}$病媒的数量。类似地，$(x, x+1]$中观察到的取款次数是$u{i, x}$向量的数量，$\kappa_i \neq 0$。
人$i$在$(x, x+1]$上贡献的确切曝光量很简单
$$
(\text { Exact Exposure }){i, x}=\left[\begin{array}{c} s_i \ \iota_i \ \kappa_i \end{array}\right]-r_i, $$其中$\left[\begin{array}{l}s_i \ \iota_i \ \kappa_i\end{array}\right]$表示$s_i, \iota_i, \kappa_i$大于零的最小值。换句话说，如果$\iota_i=\kappa_i=0$，那么那个人$i$既没有死亡也没有退出$(x, x+1]$，那么我们有(精确暴露)$i{i, x}=s_i-r_i$。但如果某人$i$死于$(x, x+1]$，那么$\iota_i<s_i$和$\kappa_i=0$，那么(确切暴露)$i_{i, x}=\iota_i-r_i$。最后，如果人$i$退出$(x, x+1)$，那么$\kappa_i<s_i$和$\iota_i=0$，然后我们有(确切曝光$)_{i, x}=\kappa_i-r_i$。
为了在Hoem时刻法下找到估计死亡概率$q_x^{\prime(d)}$的计划暴露量，对于$(x, x+1]$中退出和既不死亡也不退出的人仍然使用精确暴露量，但死亡的人暴露于年龄$x+s_i$。因此我们有
$$
(\text { Scheduled Exposure })_{i, x}=\left[\begin{array}{c}
s_i \
\kappa_i
\end{array}\right]-r_i,
$$
如果某人$i$在$(x, x+1]$中退出，则使用$\kappa_i$，否则使用$s_i$。
统计代写|生存模型代写survival model代考|Grouping
在许多情况下，可以用事件发生时的平均年龄代替事件发生时的确切年龄，以适用于所有事件发生时的确切年龄在某一年龄范围内的人。例如，考虑所有的人，其确切的$y_i$位于整数$x$和$x+1$之间。我们可以用一个共同的年龄$y^{\prime}$代替所有这些$y_i$，经常使用$y^{\prime}=x+\frac{1}{2}$作为$y_i$的假设平均值。当我们这样做时，研究的参与者已经按年龄分组，因为是那些年龄相同的人，最后生日$(x)$被一起考虑。
第二种类型的分组是按日历年龄进行的。事件发生时的日历年龄定义为在事件发生的同一日历年的生日上获得的积分年龄$y$。例如，如果一个人的出生日期是1960年9月14日，退休日期是1994年6月26日，那么退休时的日历年龄是34岁，因为这个人在退休的日历年的生日是34岁。事件的日历年龄很容易被发现
$$
C A=C Y E-C Y B,
$$
其中$C Y E$是事件发生的日历年，$C Y B$是出生的日历年。
很容易看出，日历年龄为$w$ at event的人在事件发生时的确切年龄在$(w-1, w+1)$范围内。如果$w^{\text {th }}$的生日是1月1日，而事件是同年的12月31日，那么事件的日历年龄是$w$，但确切的年龄实际上是$w+1$。相反，如果$w^{t h}$的生日是12月31日，而事件是同年的1月1日，那么事件的日历年龄仍然是$w$，但确切的年龄实际上是$w-1$。
统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。