# First-hitting-time model

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Events are often triggered when a stochastic or random process first encounters a threshold. The threshold can be a barrier, boundary or specified state of a system. The amount of time required for a stochastic process, starting from some initial state, to encounter a threshold for the first time is referred to variously as a first hitting time. In statistics, first-hitting-time models are a sub-class of survival models. The first hitting time, also called first passage time, of the barrier set $B$ with respect to an instance of a stochastic process is the time until the stochastic process first enters $B$ .

## Contents

More colloquially, a first passage time in a stochastic system, is the time taken for a state variable to reach a certain value. Understanding this metric allows one to further understand the physical system under observation, and as such has been the topic of research in very diverse fields, from economics to ecology. 

The idea that a first hitting time of a stochastic process might describe the time to occurrence of an event has a long history, starting with an interest in the first passage time of Wiener diffusion processes in economics and then in physics in the early 1900s.    Modeling the probability of financial ruin as a first passage time was an early application in the field of insurance.  An interest in the mathematical properties of first-hitting-times and statistical models and methods for analysis of survival data appeared steadily between the middle and end of the 20th century.     

## Examples

A common example of a first-hitting-time model is a ruin problem, such as Gambler's ruin. In this example, an entity (often described as a gambler or an insurance company) has an amount of money which varies randomly with time, possibly with some drift. The model considers the event that the amount of money reaches 0, representing bankruptcy. The model can answer questions such as the probability that this occurs within finite time, or the mean time until which it occurs.

First-hitting-time models can be applied to expected lifetimes, of patients or mechanical devices. When the process reaches an adverse threshold state for the first time, the patient dies, or the device breaks down.

## First passage time of a 1D Brownian particle

One of the simplest and omnipresent stochastic systems is that of the Brownian particle in one dimension. This system describes the motion of a particle which moves stochastically in one dimensional space, with equal probability of moving to the left or to the right. Given that Brownian motion is used often as a tool to understand more complex phenomena, it is important to understand the probability of a first passage time of the Brownian particle of reaching some position distant from its start location. This is done through the following means.

The probability density function (PDF) for a particle in one dimension is found by solving the one-dimensional diffusion equation. (This equation states that the position probability density diffuses outward over time. It is analogous to say, cream in a cup of coffee if the cream was all contained within some small location initially. After a long time the cream has diffused throughout the entire drink evenly.) Namely,

${\frac {\partial p(x,t\mid x_{0})}{\partial t}}=D{\frac {\partial ^{2}p(x,t\mid x_{0})}{\partial x^{2}}},$ given the initial condition $p(x,t={0}\mid x_{0})=\delta (x-x_{0})$ ; where $x(t)$ is the position of the particle at some given time, $x_{0}$ is the tagged particle's initial position, and $D$ is the diffusion constant with the S.I. units $m^{2}s^{-1}$ (an indirect measure of the particle's speed). The bar in the argument of the instantaneous probability refers to the conditional probability. The diffusion equation states that the rate of change over time in the probability of finding the particle at $x(t)$ position depends on the deceleration over distance of such probability at that position.

It can be shown that the one-dimensional PDF is

$p(x,t;x_{0})={\frac {1}{\sqrt {4\pi Dt}}}\exp \left(-{\frac {(x-x_{0})^{2}}{4Dt}}\right).$ This states that the probability of finding the particle at $x(t)$ is Gaussian, and the width of the Gaussian is time dependent. More specifically the Full Width at Half Maximum (FWHM) – technically, this is actually the Full Duration at Half Maximum as the independent variable is time – scales like

${\rm {FWHM}}\sim {\sqrt {t}}.$ Using the PDF one is able to derive the average of a given function, $L$ , at time $t$ :

$\langle L(t)\rangle \equiv \int _{-\infty }^{\infty }L(x,t)p(x,t)\,dx,$ where the average is taken over all space (or any applicable variable).

The First Passage Time Density (FPTD) is the probability that a particle has first reached a point $x_{c}$ at exactly time $t$ (not at some time during the interval up to $t$ ). This probability density is calculable from the Survival probability (a more common probability measure in statistics). Consider the absorbing boundary condition $p(x_{c},t)=0$ (The subscript c for the absorption point $x_{c}$ is an abbreviation for cliff used in many texts as an analogy to an absorption point). The PDF satisfying this boundary condition is given by

$p(x,t;x_{0},x_{c})={\frac {1}{\sqrt {4\pi Dt}}}\left(\exp \left(-{\frac {(x-x_{0})^{2}}{4Dt}}\right)-\exp \left(-{\frac {(x-(2x_{c}-x_{0}))^{2}}{4Dt}}\right)\right),$ for $x . The survival probability, the probability that the particle has remained at a position $x for all times up to $t$ , is given by

$S(t)\equiv \int _{-\infty }^{x_{c}}p(x,t;x_{0},x_{c})\,dx=\operatorname {erf} \left({\frac {x_{c}-x_{0}}{2{\sqrt {Dt}}}}\right),$ where $\operatorname {erf}$ is the error function. The relation between the Survival probability and the FPTD is as follows: the probability that a particle has reached the absorption point between times $t$ and $t+dt$ is $f(t)\,dt=S(t)-S(t+dt)$ . If one uses the first-order Taylor approximation, the definition of the FPTD follows):

$f(t)=-{\frac {\partial S(t)}{\partial t}}.$ By using the diffusion equation and integrating, the explicit FPTD is

$f(t)\equiv {\frac {|x_{c}-x_{0}|}{\sqrt {4\pi Dt^{3}}}}\exp \left(-{\frac {(x_{c}-x_{0})^{2}}{4Dt}}\right).$ The first-passage time for a Brownian particle therefore follows a Lévy distribution.

For $t\gg {\frac {(x_{c}-x_{0})^{2}}{4D}}$ , it follows from above that

$f(t)={\frac {\Delta x}{\sqrt {4\pi Dt^{3}}}}\sim t^{-3/2},$ where $\Delta x\equiv |x_{c}-x_{0}|$ . This equation states that the probability for a Brownian particle achieving a first passage at some long time (defined in the paragraph above) becomes increasingly small, but always finite.

The first moment of the FPTD diverges (as it is a so-called heavy-tailed distribution), therefore one cannot calculate the average FPT, so instead, one can calculate the typical time, the time when the FPTD is at a maximum ($\partial f/\partial t=0$ ), i.e.,

$\tau _{\rm {ty}}={\frac {\Delta x^{2}}{6D}}.$ ## First-hitting-time applications in many families of stochastic processes

First hitting times are central features of many families of stochastic processes, including Poisson processes, Wiener processes, gamma processes, and Markov chains, to name but a few. The state of the stochastic process may represent, for example, the strength of a physical system, the health of an individual, or the financial condition of a business firm. The system, individual or firm fails or experiences some other critical endpoint when the process reaches a threshold state for the first time. The critical event may be an adverse event (such as equipment failure, congested heart failure, or lung cancer) or a positive event (such as recovery from illness, discharge from hospital stay, child birth, or return to work after traumatic injury). The lapse of time until that critical event occurs is usually interpreted generically as a ‘survival time’. In some applications, the threshold is a set of multiple states so one considers competing first hitting times for reaching the first threshold in the set, as is the case when considering competing causes of failure in equipment or death for a patient.

## Threshold regression: first-hitting-time regression

Practical applications of theoretical models for first hitting times often involve regression structures. When first hitting time models are equipped with regression structures, accommodating covariate data, we call such regression structure threshold regression.  The threshold state, parameters of the process, and even time scale may depend on corresponding covariates. Threshold regression as applied to time-to-event data has emerged since the start of this century and has grown rapidly, as described in a 2006 survey article  and its references. Connections between threshold regression models derived from first hitting times and the ubiquitous Cox proportional hazards regression model  was investigated in.  Applications of threshold regression range over many fields, including the physical and natural sciences, engineering, social sciences, economics and business, agriculture, health and medicine.     

## Latent vs observable

In many real world applications, a first-hitting-time (FHT) model has three underlying components: (1) a parent stochastic process$\{X(t)\}\,\,$ , which might be latent, (2) a threshold (or the barrier) and (3) a time scale. The first hitting time is defined as the time when the stochastic process first reaches the threshold. It is very important to distinguish whether the sample path of the parent process is latent (i.e., unobservable) or observable, and such distinction is a characteristic of the FHT model. By far, latent processes are most common. To give an example, we can use a Wiener process $\{X(t),t\geq 0\,\}\,$ as the parent stochastic process. Such Wiener process can be defined with the mean parameter ${\mu }\,\,$ , the variance parameter ${\sigma ^{2}}\,\,$ , and the initial value $X(0)=x_{0}>0\,$ .

## Operational or analytical time scale

The time scale of the stochastic process may be calendar or clock time or some more operational measure of time progression, such as mileage of a car, accumulated wear and tear on a machine component or accumulated exposure to toxic fumes. In many applications, the stochastic process describing the system state is latent or unobservable and its properties must be inferred indirectly from censored time-to-event data and/or readings taken over time on correlated processes, such as marker processes. The word ‘regression’ in threshold regression refers to first-hitting-time models in which one or more regression structures are inserted into the model in order to connect model parameters to explanatory variables or covariates. The parameters given regression structures may be parameters of the stochastic process, the threshold state and/or the time scale itself.

## Related Research Articles Brownian motion, or pedesis, is the random motion of particles suspended in a medium. Fick's laws of diffusion describe diffusion and were derived by Adolf Fick in 1855. They can be used to solve for the diffusion coefficient, D. Fick's first law can be used to derive his second law which in turn is identical to the diffusion equation. In mathematics, the Wiener process is a real valued continuous-time stochastic process named in honor of American mathematician Norbert Wiener for his investigations on the mathematical properties of the one-dimensional Brownian motion. It is often also called Brownian motion due to its historical connection with the physical process of the same name originally observed by Scottish botanist Robert Brown. It is one of the best known Lévy processes and occurs frequently in pure and applied mathematics, economics, quantitative finance, evolutionary biology, and physics.

In physics, a Langevin equation is a stochastic differential equation describing the time evolution of a subset of the degrees of freedom. These degrees of freedom typically are collective (macroscopic) variables changing only slowly in comparison to the other (microscopic) variables of the system. The fast (microscopic) variables are responsible for the stochastic nature of the Langevin equation. One application is to Brownian motion, calculating the statistics of the random motion of a small particle in a fluid due to collisions with the surrounding molecules in thermal motion. In statistical mechanics, the Fokker–Planck equation is a partial differential equation that describes the time evolution of the probability density function of the velocity of a particle under the influence of drag forces and random forces, as in Brownian motion. The equation can be generalized to other observables as well. It is named after Adriaan Fokker and Max Planck, and is also known as the Kolmogorov forward equation, after Andrey Kolmogorov, who independently discovered the concept in 1931. When applied to particle position distributions, it is better known as the Smoluchowski equation, and in this context it is equivalent to the convection–diffusion equation. The case with zero diffusion is known in statistical mechanics as the Liouville equation. The Fokker–Planck equation is obtained from the master equation through Kramers–Moyal expansion. A geometric Brownian motion (GBM) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion with drift. It is an important example of stochastic processes satisfying a stochastic differential equation (SDE); in particular, it is used in mathematical finance to model stock prices in the Black–Scholes model.

In probability theory and statistics, a Gaussian process is a stochastic process, such that every finite collection of those random variables has a multivariate normal distribution, i.e. every finite linear combination of them is normally distributed. The distribution of a Gaussian process is the joint distribution of all those random variables, and as such, it is a distribution over functions with a continuous domain, e.g. time or space. In probability theory, the Girsanov theorem describes how the dynamics of stochastic processes change when the original measure is changed to an equivalent probability measure. The theorem is especially important in the theory of financial mathematics as it tells how to convert from the physical measure, which describes the probability that an underlying instrument will take a particular value or values, to the risk-neutral measure which is a very useful tool for pricing derivatives on the underlying instrument. A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, resulting in a solution which is also a stochastic process. SDEs are used to model various phenomena such as unstable stock prices or physical systems subject to thermal fluctuations. Typically, SDEs contain a variable which represents random white noise calculated as the derivative of Brownian motion or the Wiener process. However, other types of random behaviour are possible, such as jump processes. Random differential equations are conjugate to stochastic differential equations. In mathematics, the Ornstein–Uhlenbeck process is a stochastic process with applications in financial mathematics and the physical sciences. Its original application in physics was as a model for the velocity of a massive Brownian particle under the influence of friction. It is named after Leonard Ornstein and George Eugene Uhlenbeck. In probability theory, the inverse Gaussian distribution is a two-parameter family of continuous probability distributions with support on (0,∞). In probability theory, the Schramm–Loewner evolution with parameter κ, also known as stochastic Loewner evolution (SLEκ), is a family of random planar curves that have been proven to be the scaling limit of a variety of two-dimensional lattice models in statistical mechanics. Given a parameter κ and a domain in the complex plane U, it gives a family of random curves in U, with κ controlling how much the curve turns. There are two main variants of SLE, chordal SLE which gives a family of random curves from two fixed boundary points, and radial SLE, which gives a family of random curves from a fixed boundary point to a fixed interior point. These curves are defined to satisfy conformal invariance and a domain Markov property. In mathematical finance, the Cox–Ingersoll–Ross (CIR) model describes the evolution of interest rates. It is a type of "one factor model" as it describes interest rate movements as driven by only one source of market risk. The model can be used in the valuation of interest rate derivatives. It was introduced in 1985 by John C. Cox, Jonathan E. Ingersoll and Stephen A. Ross as an extension of the Vasicek model.

In mathematics — specifically, in stochastic analysis — an Itô diffusion is a solution to a specific type of stochastic differential equation. That equation is similar to the Langevin equation used in physics to describe the Brownian motion of a particle subjected to a potential in a viscous fluid. Itô diffusions are named after the Japanese mathematician Kiyosi Itô.

The narrow escape problem is a ubiquitous problem in biology, biophysics and cellular biology. In probability theory a Brownian excursion process is a stochastic process that is closely related to a Wiener process. Realisations of Brownian excursion processes are essentially just realizations of a Wiener process selected to satisfy certain conditions. In particular, a Brownian excursion process is a Wiener process conditioned to be positive and to take the value 0 at time 1. Alternatively, it is a Brownian bridge process conditioned to be positive. BEPs are important because, among other reasons, they naturally arise as the limit process of a number of conditional functional central limit theorems.

In queueing theory, a discipline within the mathematical theory of probability, the M/M/∞ queue is a multi-server queueing model where every arrival experiences immediate service and does not wait. In Kendall's notation it describes a system where arrivals are governed by a Poisson process, there are infinitely many servers, so jobs do not need to wait for a server. Each job has an exponentially distributed service time. It is a limit of the M/M/c queue model where the number of servers c becomes very large.

In statistical mechanics, the mean squared displacement is a measure of the deviation of the position of a particle with respect to a reference position over time. It is the most common measure of the spatial extent of random motion, and can be thought of as measuring the portion of the system "explored" by the random walker. In the realm of biophysics and environmental engineering, the Mean Squared Displacement is measured over time to determine if a particle is spreading solely due to diffusion, or if an advective force is also contributing. Another relevant concept, the Variance-Related Diameter, is also used in studying the transportation and mixing phenomena in the realm of environmental engineering. It prominently appears in the Debye–Waller factor and in the Langevin equation.

In queueing theory, a discipline within the mathematical theory of probability, a heavy traffic approximation is the matching of a queueing model with a diffusion process under some limiting conditions on the model's parameters. The first such result was published by John Kingman who showed that when the utilisation parameter of an M/M/1 queue is near 1 a scaled version of the queue length process can be accurately approximated by a reflected Brownian motion.

The redundancy principle in biology expresses the need of many copies of the same entity to fulfill a biological function. Examples are numerous: disproportionate numbers of spermatozoa during fertilization compared to one egg, large number of neurotransmitters released during neuronal communication compared to the number of receptors, large numbers of released calcium ions during transient in cells and many more in molecular and cellular transduction or gene activation and cell signaling. This redundancy is particularly relevant when the sites of activation is physically separated from the initial position of the molecular messengers. The redundancy is often generated for the purpose of resolving the time constraint of fast-activating pathways. It can be expressed in terms of the theory of extreme statistics to determine its laws and quantify how shortest paths are selected. The main goal is to estimate these large numbers from physical principles and mathematical derivations.

1. Redner 2001
2. Bachelier 1900
3. Von E 1900
4. Smoluchowski 1915
5. Lundberg 1903
6. Tweedie 1945
7. Tweedie 1957–1
8. Tweedie 1957–2
9. Whitmore 1970
10. Lancaster 1972
11. Lee 2006
12. Lee 2006
13. Cox 1972
14. Lee 2010
15. Aaron 2010
16. Chambaz 2014
17. Aaron 2015
18. He 2015
19. Hou 2016
• Whitmore, G. A. (1986). "First passage time models for duration data regression structures and competing risks". The Statistician. 35: 207–219. doi:10.2307/2987525. JSTOR   2987525.
• Whitmore, G. A. (1995). "Estimating degradation by a Wiener diffusion process subject to measurement error". Lifetime Data Analysis. 1 (3): 307–319. doi:10.1007/BF00985762.
• Whitmore, G. A.; Crowder, M. J.; Lawless, J. F. (1998). "Failure inference from a marker process based on a bivariate Wiener model". Lifetime Data Analysis. 4 (3): 229–251. doi:10.1023/A:1009617814586.
• Redner, S. (2001). A Guide to First-Passage Processes. Cambridge University Press. ISBN   0-521-65248-0.
• Lee, M.-L. T.; Whitmore, G. A. (2006). "Threshold regression for survival analysis: Modeling event times by a stochastic process". Statistical Science. 21 (4): 501–513. arXiv:. doi:10.1214/088342306000000330.
• Bachelier, L. (1900). "Théorie de la Spéculation". Annales Scientifiques de l'École Normale Supérieure. 3 (17): 21–86.
• Schrodinger, E. (1915). "Zur Theorie der Fall-und Steigversuche an Teilchen mit Brownscher Bewegung". Physikalische Zeitschrift. 16: 289–295.
• Smoluchowski, M. V. (1915). "Notiz über die Berechning der Brownschen Molkularbewegung bei des Ehrenhaft-millikanchen Versuchsanordnung". Physikalische Zeitschrift. 16: 318–321.
• Lundberg, F. (1903). Approximerad Framställning av Sannolikehetsfunktionen, Återförsäkering av Kollektivrisker. Almqvist & Wiksell, Uppsala.
• Tweedie, M. C. K. (1945). "Inverse statistical variates". Nature. 155: 453. Bibcode:1945Natur.155..453T. doi:.
• Tweedie, M. C. K. (1957). "Statistical properties of inverse Gaussian distributions – I". Annals of Mathematical Statistics. 28: 362–377. doi:.
• Tweedie, M. C. K. (1957). "Statistical properties of inverse Gaussian distributions – II". Annals of Mathematical Statistics. 28: 696–705. doi:10.1214/aoms/1177706881.
• Whitmore, G. A.; Neufeldt, A. H. (1970). "An application of statistical models in mental health research". Bull. Math. Biophys. 32: 563–579.
• Lancaster, T. (1972). "A stochastic model for the duration of a strike". J. Roy. Statist. Soc. Ser. A. 135: 257–271.
• Cox, D. R. (1972). "Regression models and life tables (with discussion)". J R Stat Soc Ser B. 187: 187–230.
• Lee, M.-L. T.; Whitmore, G. A. (2010). "Threshold Proportional hazards and threshold regression: their theoretical and practical connections". Lifetime Data Analysis. 16: 196–214. doi:10.1007/s10985-009-9138-0. PMC  . PMID   19960249.
• Aaron, S. D.; Ramsay, T.; Vandemheen, K.; Whitmore, G. A. (2010). "A threshold regression model for recurrent exacerbations in chronic obstructive pulmonary disease". Journal of Clinical Epidemiology. 63: 1324–1331. doi:10.1016/j.jclinepi.2010.05.007.
• Chambaz, A.; Choudat, D.; Huber, C.; Pairon, J.; Van der Lann, M. J. (2014). "Analysis of occupational exposure to asbestos based on threshold regression modeling of case-control data". Biostatistics. 15: 327–340. doi:.
• Aaron, S. D.; Stephenson, A. L.; Cameron, D. W.; Whitmore, G. A. (2015). "A statistical model to predict one-year risk of death in patients with cystic fibrosis". Journal of Clinical Epidemiology. 68: 1336–1345. doi:10.1016/j.jclinepi.2014.12.010.
• He, X.; Whitmore, G. A.; Loo, G. Y.; Hochberg, M. C.; Lee, M.-L. T. (2015). "A model for time to fracture with a shock stream superimposed on progressive degradation: the Study of Osteoporotic Fractures". Statistics in Medicine. 34: 652–663. doi:10.1002/sim.6356. PMC  . PMID   25376757.
• Hou, W.-H.; Chuang, H.-Y.; Lee, M.-L. T. (2016). "A threshold regression model to predict return to work after traumatic limb injury". Injury. 47: 483–489. doi:10.1016/j.injury.2015.11.032.