Convergence of random variables

"Stochastic convergence" formalizes the idea that a sequence of essentially random or unpredictable events can sometimes be expected to settle into a pattern. The pattern may for instance be

• Convergence in the classical sense to a fixed value, perhaps itself coming from a random event
• An increasing similarity of outcomes to what a purely deterministic function would produce
• An increasing preference towards a certain outcome
• An increasing "aversion" against straying far away from a certain outcome
• That the probability distribution describing the next outcome may grow increasingly similar to a certain distribution

Some less obvious, more theoretical patterns could be

• That the series formed by calculating the expected value of the outcome's distance from a particular value may converge to 0
• That the variance of the random variable describing the next event grows smaller and smaller.

These other types of patterns that may arise are reflected in the different types of stochastic convergence that have been studied.

While the above discussion has related to the convergence of a single series to a limiting value, the notion of the convergence of two series towards each other is also important, but this is easily handled by studying the sequence defined as either the difference or the ratio of the two series.

For example, if the average of n independent random variables ${\displaystyle Y_{i},\ i=1,\dots ,n}$, all having the same finite mean and variance, is given by

${\displaystyle X_{n}={\frac {1}{n}}\sum _{i=1}^{n}Y_{i}\,,}$

then as ${\displaystyle n}$ tends to infinity, ${\displaystyle X_{n}}$ converges in probability (see below) to the common mean, ${\displaystyle \mu }$, of the random variables ${\displaystyle Y_{i}}$. This result is known as the weak law of large numbers. Other forms of convergence are important in other useful theorems, including the central limit theorem.

Throughout the following, we assume that ${\displaystyle (X_{n})}$ is a sequence of random variables, and ${\displaystyle X}$ is a random variable, and all of them are defined on the same probability space ${\displaystyle (\Omega ,{\mathcal {F}},\mathbb {P} )}$.

Examples of convergence in distribution

Dice factory
Suppose a new dice factory has just been built. The first few dice come out quite biased, due to imperfections in the production process. The outcome from tossing any of them will follow a distribution markedly different from the desired uniform distribution. As the factory is improved, the dice become less and less loaded, and the outcomes from tossing a newly produced die will follow the uniform distribution more and more closely.
Tossing coins
Let Xn be the fraction of heads after tossing up an unbiased coin n times. Then X1 has the Bernoulli distribution with expected value μ = 0.5 and variance σ2 = 0.25. The subsequent random variables X2, X3, ... will all be distributed binomially. As n grows larger, this distribution will gradually start to take shape more and more similar to the bell curve of the normal distribution. If we shift and rescale Xn appropriately, then ${\displaystyle \scriptstyle Z_{n}={\frac {\sqrt {n}}{\sigma }}(X_{n}-\mu )}$ will be converging in distribution to the standard normal, the result that follows from the celebrated central limit theorem.
Graphic example
Suppose {Xi} is an iid sequence of uniform U(−1, 1) random variables. Let ${\displaystyle \scriptstyle Z_{n}={\scriptscriptstyle {\frac {1}{\sqrt {n}}}}\sum _{i=1}^{n}X_{i}}$ be their (normalized) sums. Then according to the central limit theorem, the distribution of Zn approaches the normal N(0, ⁠1/3⁠) distribution. This convergence is shown in the picture: as n grows larger, the shape of the probability density function gets closer and closer to the Gaussian curve.

Loosely, with this mode of convergence, we increasingly expect to see the next outcome in a sequence of random experiments becoming better and better modeled by a given probability distribution. More precisely, the distribution of the associated random variable in the sequence becomes arbitrarily close to a specified fixed distribution.

Convergence in distribution is the weakest form of convergence typically discussed, since it is implied by all other types of convergence mentioned in this article. However, convergence in distribution is very frequently used in practice; most often it arises from application of the central limit theorem.

A sequence ${\displaystyle X_{1},X_{2},\ldots }$ of real-valued random variables, with cumulative distribution functions ${\displaystyle F_{1},F_{2},\ldots }$, is said to converge in distribution, or converge weakly, or converge in law to a random variable X with cumulative distribution function F if

${\displaystyle \lim _{n\to \infty }F_{n}(x)=F(x),}$

for every number ${\displaystyle x\in \mathbb {R} }$ at which ${\displaystyle F}$ is continuous.

The requirement that only the continuity points of ${\displaystyle F}$ should be considered is essential. For example, if ${\displaystyle X_{n}}$ are distributed uniformly on intervals ${\displaystyle \left(0,{\frac {1}{n}}\right)}$, then this sequence converges in distribution to the degenerate random variable ${\displaystyle X=0}$. Indeed, ${\displaystyle F_{n}(x)=0}$ for all ${\displaystyle n}$ when ${\displaystyle x\leq 0}$, and ${\displaystyle F_{n}(x)=1}$ for all ${\displaystyle x\geq {\frac {1}{n}}}$when ${\displaystyle n>0}$. However, for this limiting random variable ${\displaystyle F(0)=1}$, even though ${\displaystyle F_{n}(0)=0}$ for all ${\displaystyle n}$. Thus the convergence of cdfs fails at the point ${\displaystyle x=0}$ where ${\displaystyle F}$ is discontinuous.

Convergence in distribution may be denoted as

${\displaystyle {\begin{aligned}{}&X_{n}\ \xrightarrow {d} \ X,\ \ X_{n}\ \xrightarrow {\mathcal {D}} \ X,\ \ X_{n}\ \xrightarrow {\mathcal {L}} \ X,\ \ X_{n}\ \xrightarrow {d} \ {\mathcal {L}}_{X},\\&X_{n}\rightsquigarrow X,\ \ X_{n}\Rightarrow X,\ \ {\mathcal {L}}(X_{n})\to {\mathcal {L}}(X),\\\end{aligned}}}$

1

where ${\displaystyle \scriptstyle {\mathcal {L}}_{X}}$ is the law (probability distribution) of X. For example, if X is standard normal we can write ${\displaystyle X_{n}\,{\xrightarrow {d}}\,{\mathcal {N}}(0,\,1)}$.

For random vectors ${\displaystyle \left\{X_{1},X_{2},\dots \right\}\subset \mathbb {R} ^{k}}$ the convergence in distribution is defined similarly. We say that this sequence converges in distribution to a random k-vector X if

${\displaystyle \lim _{n\to \infty }\mathbb {P} (X_{n}\in A)=\mathbb {P} (X\in A)}$

for every ${\displaystyle A\subset \mathbb {R} ^{k}}$ which is a continuity set of X.

The definition of convergence in distribution may be extended from random vectors to more general random elements in arbitrary metric spaces, and even to the “random variables” which are not measurable — a situation which occurs for example in the study of empirical processes. This is the “weak convergence of laws without laws being defined” — except asymptotically.^[1]

In this case the term weak convergence is preferable (see weak convergence of measures), and we say that a sequence of random elements {X_n} converges weakly to X (denoted as X_n ⇒ X) if

${\displaystyle \mathbb {E} ^{*}h(X_{n})\to \mathbb {E} \,h(X)}$

for all continuous bounded functions h.^[2] Here E* denotes the outer expectation, that is the expectation of a “smallest measurable function g that dominates h(X_n)”.

• Since ${\displaystyle F(a)=\mathbb {P} (X\leq a)}$, the convergence in distribution means that the probability for X_n to be in a given range is approximately equal to the probability that the value of X is in that range, provided n is sufficiently large.
• In general, convergence in distribution does not imply that the sequence of corresponding probability density functions will also converge. As an example one may consider random variables with densities f_n(x) = (1 + cos(2πnx))1_(0,1). These random variables converge in distribution to a uniform U(0, 1), whereas their densities do not converge at all.^[3]
  • However, according to Scheffé’s theorem, convergence of the probability density functions implies convergence in distribution.^[4]
• The portmanteau lemma provides several equivalent definitions of convergence in distribution. Although these definitions are less intuitive, they are used to prove a number of statistical theorems. The lemma states that {X_n} converges in distribution to X if and only if any of the following statements are true:^[5]
  • ${\displaystyle \mathbb {P} (X_{n}\leq x)\to \mathbb {P} (X\leq x)}$ for all continuity points of ${\displaystyle x\mapsto \mathbb {P} (X\leq x)}$;
  • ${\displaystyle \mathbb {E} f(X_{n})\to \mathbb {E} f(X)}$ for all bounded, continuous functions ${\displaystyle f}$ (where ${\displaystyle \mathbb {E} }$ denotes the expected value operator);
  • ${\displaystyle \mathbb {E} f(X_{n})\to \mathbb {E} f(X)}$ for all bounded, Lipschitz functions ${\displaystyle f}$;
  • ${\displaystyle \lim \inf \mathbb {E} f(X_{n})\geq \mathbb {E} f(X)}$ for all nonnegative, continuous functions ${\displaystyle f}$;
  • ${\displaystyle \lim \inf \mathbb {P} (X_{n}\in G)\geq \mathbb {P} (X\in G)}$ for every open set ${\displaystyle G}$;
  • ${\displaystyle \lim \sup \mathbb {P} (X_{n}\in F)\leq \mathbb {P} (X\in F)}$ for every closed set ${\displaystyle F}$;
  • ${\displaystyle \mathbb {P} (X_{n}\in B)\to \mathbb {P} (X\in B)}$ for all continuity sets ${\displaystyle B}$ of random variable ${\displaystyle X}$;
  • ${\displaystyle \limsup \mathbb {E} f(X_{n})\leq \mathbb {E} f(X)}$ for every upper semi-continuous function ${\displaystyle f}$ bounded above;^{[citation needed]}
  • ${\displaystyle \liminf \mathbb {E} f(X_{n})\geq \mathbb {E} f(X)}$ for every lower semi-continuous function ${\displaystyle f}$ bounded below.^{[citation needed]}
• The continuous mapping theorem states that for a continuous function g, if the sequence {X_n} converges in distribution to X, then {g(X_n)} converges in distribution to g(X).
  • Note however that convergence in distribution of {X_n} to X and {Y_n} to Y does in general not imply convergence in distribution of {X_n + Y_n} to X + Y or of {X_nY_n} to XY.
• Lévy’s continuity theorem: The sequence {X_n} converges in distribution to X if and only if the sequence of corresponding characteristic functions {φ_n} converges pointwise to the characteristic function φ of X.
• Convergence in distribution is metrizable by the Lévy–Prokhorov metric.
• A natural link to convergence in distribution is the Skorokhod's representation theorem.

The basic idea behind this type of convergence is that the probability of an “unusual” outcome becomes smaller and smaller as the sequence progresses.

The concept of convergence in probability is used very often in statistics. For example, an estimator is called consistent if it converges in probability to the quantity being estimated. Convergence in probability is also the type of convergence established by the weak law of large numbers.

A sequence {X_n} of random variables converges in probability towards the random variable X if for all ε > 0

${\displaystyle \lim _{n\to \infty }\mathbb {P} {\big (}|X_{n}-X|>\varepsilon {\big )}=0.}$

More explicitly, let P_n(ε) be the probability that X_n is outside the ball of radius ε centered at X. Then X_n is said to converge in probability to X if for any ε > 0 and any δ > 0 there exists a number N (which may depend on ε and δ) such that for all n ≥ N, P_n(ε) < δ (the definition of limit).

Notice that for the condition to be satisfied, it is not possible that for each n the random variables X and X_n are independent (and thus convergence in probability is a condition on the joint cdf's, as opposed to convergence in distribution, which is a condition on the individual cdf's), unless X is deterministic like for the weak law of large numbers. At the same time, the case of a deterministic X cannot, whenever the deterministic value is a discontinuity point (not isolated), be handled by convergence in distribution, where discontinuity points have to be explicitly excluded.

Convergence in probability is denoted by adding the letter p over an arrow indicating convergence, or using the "plim" probability limit operator:

${\displaystyle X_{n}\ \xrightarrow {p} \ X,\ \ X_{n}\ \xrightarrow {P} \ X,\ \ {\underset {n\to \infty }{\operatorname {plim} }}\,X_{n}=X.}$

2

For random elements {X_n} on a separable metric space (S, d), convergence in probability is defined similarly by^[6]

${\displaystyle \forall \varepsilon >0,\mathbb {P} {\big (}d(X_{n},X)\geq \varepsilon {\big )}\to 0.}$

• Convergence in probability implies convergence in distribution.^[proof]
• In the opposite direction, convergence in distribution implies convergence in probability when the limiting random variable X is a constant.^[proof]
• Convergence in probability does not imply almost sure convergence.^[proof]
• The continuous mapping theorem states that for every continuous function ${\displaystyle g}$, if ${\textstyle X_{n}\xrightarrow {p} X}$, then also ${\textstyle g(X_{n})\xrightarrow {p} g(X)}$.
• Convergence in probability defines a topology on the space of random variables over a fixed probability space. This topology is metrizable by the Ky Fan metric:^[7] $${\displaystyle d(X,Y)=\inf \!{\big \{}\varepsilon >0:\ \mathbb {P} {\big (}|X-Y|>\varepsilon {\big )}\leq \varepsilon {\big \}}}$$ or alternately by this metric $${\displaystyle d(X,Y)=\mathbb {E} \left[\min(|X-Y|,1)\right].}$$

Not every sequence of random variables which converges to another random variable in distribution also converges in probability to that random variable. As an example, consider a sequence of standard normal random variables ${\displaystyle X_{n}}$ and a second sequence ${\displaystyle Y_{n}=(-1)^{n}X_{n}}$. Notice that the distribution of ${\displaystyle Y_{n}}$ is equal to the distribution of ${\displaystyle X_{n}}$ for all ${\displaystyle n}$, but: $${\displaystyle P(|X_{n}-Y_{n}|\geq \epsilon )=P(|X_{n}|\cdot |(1-(-1)^{n})|\geq \epsilon )}$$

which does not converge to ${\displaystyle 0}$. So we do not have convergence in probability.

This is the type of stochastic convergence that is most similar to pointwise convergence known from elementary real analysis.

To say that the sequence X_n converges almost surely or almost everywhere or with probability 1 or strongly towards X means that $${\displaystyle \mathbb {P} \!\left(\lim _{n\to \infty }\!X_{n}=X\right)=1.}$$

This means that the values of X_n approach the value of X, in the sense that events for which X_n does not converge to X have probability 0 (see Almost surely). Using the probability space ${\displaystyle (\Omega ,{\mathcal {F}},\mathbb {P} )}$ and the concept of the random variable as a function from Ω to R, this is equivalent to the statement $${\displaystyle \mathbb {P} {\Bigl (}\omega \in \Omega :\lim _{n\to \infty }X_{n}(\omega )=X(\omega ){\Bigr )}=1.}$$

Using the notion of the limit superior of a sequence of sets, almost sure convergence can also be defined as follows: $${\displaystyle \mathbb {P} {\Bigl (}\limsup _{n\to \infty }{\bigl \{}\omega \in \Omega :|X_{n}(\omega )-X(\omega )|>\varepsilon {\bigr \}}{\Bigr )}=0\quad {\text{for all}}\quad \varepsilon >0.}$$

Almost sure convergence is often denoted by adding the letters a.s. over an arrow indicating convergence:

${\displaystyle {\overset {}{X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X.}}}$

3

For generic random elements {X_n} on a metric space ${\displaystyle (S,d)}$, convergence almost surely is defined similarly: $${\displaystyle \mathbb {P} {\Bigl (}\omega \in \Omega \colon \,d{\big (}X_{n}(\omega ),X(\omega ){\big )}\,{\underset {n\to \infty }{\longrightarrow }}\,0{\Bigr )}=1}$$

• Almost sure convergence implies convergence in probability (by Fatou's lemma), and hence implies convergence in distribution. It is the notion of convergence used in the strong law of large numbers.
• The concept of almost sure convergence does not come from a topology on the space of random variables. This means there is no topology on the space of random variables such that the almost surely convergent sequences are exactly the converging sequences with respect to that topology. In particular, there is no metric of almost sure convergence.

Consider a sequence ${\displaystyle \{X_{n}\}}$ of independent random variables such that ${\displaystyle P(X_{n}=1)={\frac {1}{n}}}$ and ${\displaystyle P(X_{n}=0)=1-{\frac {1}{n}}}$. For ${\displaystyle 0<\varepsilon <1/2}$ we have ${\displaystyle P(|X_{n}|\geq \varepsilon )={\frac {1}{n}}}$ which converges to ${\displaystyle 0}$ hence ${\displaystyle X_{n}\to 0}$ in probability.

Since ${\displaystyle \sum _{n\geq 1}P(X_{n}=1)\to \infty }$ and the events ${\displaystyle \{X_{n}=1\}}$ are independent, second Borel Cantelli Lemma ensures that ${\displaystyle P(\limsup _{n}\{X_{n}=1\})=1}$ hence the sequence ${\displaystyle \{X_{n}\}}$ does not converge to ${\displaystyle 0}$ almost everywhere (in fact the set on which this sequence does not converge to ${\displaystyle 0}$ has probability ${\displaystyle 1}$).

To say that the sequence of random variables (X_n) defined over the same probability space (i.e., a random process) converges surely or everywhere or pointwise towards X means

$${\displaystyle \forall \omega \in \Omega \colon \ \lim _{n\to \infty }X_{n}(\omega )=X(\omega ),}$$

where Ω is the sample space of the underlying probability space over which the random variables are defined.

This is the notion of pointwise convergence of a sequence of functions extended to a sequence of random variables. (Note that random variables themselves are functions).

$${\displaystyle \left\{\omega \in \Omega :\lim _{n\to \infty }X_{n}(\omega )=X(\omega )\right\}=\Omega .}$$

Sure convergence of a random variable implies all the other kinds of convergence stated above, but there is no payoff in probability theory by using sure convergence compared to using almost sure convergence. The difference between the two only exists on sets with probability zero. This is why the concept of sure convergence of random variables is very rarely used.

Given a real number r ≥ 1, we say that the sequence X_n converges in the r-th mean (or in the L^r-norm) towards the random variable X, if the r-th absolute moments ${\displaystyle \mathbb {E} }$(|X_n|^r ) and ${\displaystyle \mathbb {E} }$(|X|^r ) of X_n and X exist, and

${\displaystyle \lim _{n\to \infty }\mathbb {E} \left(|X_{n}-X|^{r}\right)=0,}$

where the operator E denotes the expected value. Convergence in r-th mean tells us that the expectation of the r-th power of the difference between ${\displaystyle X_{n}}$ and ${\displaystyle X}$ converges to zero.

This type of convergence is often denoted by adding the letter L^r over an arrow indicating convergence:

${\displaystyle {\overset {}{X_{n}\,{\xrightarrow {L^{r}}}\,X.}}}$

4

The most important cases of convergence in r-th mean are:

• When X_n converges in r-th mean to X for r = 1, we say that X_n converges in mean to X.
• When X_n converges in r-th mean to X for r = 2, we say that X_n converges in mean square (or in quadratic mean) to X.

Convergence in the r-th mean, for r ≥ 1, implies convergence in probability (by Markov's inequality). Furthermore, if r > s ≥ 1, convergence in r-th mean implies convergence in s-th mean. Hence, convergence in mean square implies convergence in mean.

Additionally,

${\displaystyle {\overset {}{X_{n}\xrightarrow {L^{r}} X}}\quad \Rightarrow \quad \lim _{n\to \infty }\mathbb {E} [|X_{n}|^{r}]=\mathbb {E} [|X|^{r}].}$

The converse is not necessarily true, however it is true if ${\displaystyle {\overset {}{X_{n}\,\xrightarrow {p} \,X}}}$ (by a more general version of Scheffé's lemma).

Provided the probability space is complete:

• If ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{p}}}\ X}$ and ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{p}}}\ Y}$, then ${\displaystyle X=Y}$ almost surely.
• If ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{\text{a.s.}}}}\ X}$ and ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{\text{a.s.}}}}\ Y}$, then ${\displaystyle X=Y}$ almost surely.
• If ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{L^{r}}}}\ X}$ and ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{L^{r}}}}\ Y}$, then ${\displaystyle X=Y}$ almost surely.
• If ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{p}}}\ X}$ and ${\displaystyle Y_{n}\ {\xrightarrow {\overset {}{p}}}\ Y}$, then ${\displaystyle aX_{n}+bY_{n}\ {\xrightarrow {\overset {}{p}}}\ aX+bY}$ (for any real numbers a and b) and ${\displaystyle X_{n}Y_{n}{\xrightarrow {\overset {}{p}}}\ XY}$.
• If ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{\text{a.s.}}}}\ X}$ and ${\displaystyle Y_{n}\ {\xrightarrow {\overset {}{\text{a.s.}}}}\ Y}$, then ${\displaystyle aX_{n}+bY_{n}\ {\xrightarrow {\overset {}{\text{a.s.}}}}\ aX+bY}$ (for any real numbers a and b) and ${\displaystyle X_{n}Y_{n}{\xrightarrow {\overset {}{\text{a.s.}}}}\ XY}$.
• If ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{L^{r}}}}\ X}$ and ${\displaystyle Y_{n}\ {\xrightarrow {\overset {}{L^{r}}}}\ Y}$, then ${\displaystyle aX_{n}+bY_{n}\ {\xrightarrow {\overset {}{L^{r}}}}\ aX+bY}$ (for any real numbers a and b).
• None of the above statements are true for convergence in distribution.

The chain of implications between the various notions of convergence are noted in their respective sections. They are, using the arrow notation:

${\displaystyle {\begin{matrix}{\xrightarrow {\overset {}{L^{s}}}}&{\underset {s>r\geq 1}{\Rightarrow }}&{\xrightarrow {\overset {}{L^{r}}}}&&\\&&\Downarrow &&\\{\xrightarrow {\text{a.s.}}}&\Rightarrow &{\xrightarrow {p}}&\Rightarrow &{\xrightarrow {d}}\end{matrix}}}$

These properties, together with a number of other special cases, are summarized in the following list:

• Almost sure convergence implies convergence in probability:^[8][proof]

  ${\displaystyle X_{n}\ {\xrightarrow {\text{a.s.}}}\ X\quad \Rightarrow \quad X_{n}\ {\xrightarrow {\overset {}{p}}}\ X}$

• Convergence in probability implies there exists a sub-sequence ${\displaystyle (n_{k})}$ which almost surely converges:^[9]

  ${\displaystyle X_{n}\ \xrightarrow {\overset {}{p}} \ X\quad \Rightarrow \quad X_{n_{k}}\ \xrightarrow {\text{a.s.}} \ X}$

• Convergence in probability implies convergence in distribution:^[8][proof]

  ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{p}}}\ X\quad \Rightarrow \quad X_{n}\ {\xrightarrow {\overset {}{d}}}\ X}$

• Convergence in r-th order mean implies convergence in probability:

  ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{L^{r}}}}\ X\quad \Rightarrow \quad X_{n}\ {\xrightarrow {\overset {}{p}}}\ X}$

• Convergence in r-th order mean implies convergence in lower order mean, assuming that both orders are greater than or equal to one:

  ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{L^{r}}}}\ X\quad \Rightarrow \quad X_{n}\ {\xrightarrow {\overset {}{L^{s}}}}\ X,}$ provided r ≥ s ≥ 1.

• If X_n converges in distribution to a constant c, then X_n converges in probability to c:^[8][proof]

  ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{d}}}\ c\quad \Rightarrow \quad X_{n}\ {\xrightarrow {\overset {}{p}}}\ c,}$ provided c is a constant.

• If X_n converges in distribution to X and the difference between X_n and Y_n converges in probability to zero, then Y_n also converges in distribution to X:^[8][proof]

  ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{d}}}\ X,\ \ |X_{n}-Y_{n}|\ {\xrightarrow {\overset {}{p}}}\ 0\ \quad \Rightarrow \quad Y_{n}\ {\xrightarrow {\overset {}{d}}}\ X}$

• If X_n converges in distribution to X and Y_n converges in distribution to a constant c, then the joint vector (X_n, Y_n) converges in distribution to ⁠${\displaystyle (X,c)}$⁠:^[8][proof]

  ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{d}}}\ X,\ \ Y_{n}\ {\xrightarrow {\overset {}{d}}}\ c\ \quad \Rightarrow \quad (X_{n},Y_{n})\ {\xrightarrow {\overset {}{d}}}\ (X,c)}$ provided c is a constant.

  Note that the condition that Y_n converges to a constant is important, if it were to converge to a random variable Y then we wouldn't be able to conclude that (X_n, Y_n) converges to ⁠${\displaystyle (X,Y)}$⁠.

• If X_n converges in probability to X and Y_n converges in probability to Y, then the joint vector (X_n, Y_n) converges in probability to (X, Y):^[8][proof]

  ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{p}}}\ X,\ \ Y_{n}\ {\xrightarrow {\overset {}{p}}}\ Y\ \quad \Rightarrow \quad (X_{n},Y_{n})\ {\xrightarrow {\overset {}{p}}}\ (X,Y)}$

• If X_n converges in probability to X, and if P(|X_n| ≤ b) = 1 for all n and some b, then X_n converges in rth mean to X for all r ≥ 1. In other words, if X_n converges in probability to X and all random variables X_n are almost surely bounded above and below, then X_n converges to X also in any rth mean.^[10]
• Almost sure representation. Usually, convergence in distribution does not imply convergence almost surely. However, for a given sequence {X_n} which converges in distribution to X₀ it is always possible to find a new probability space (Ω, F, P) and random variables {Y_n, n = 0, 1, ...} defined on it such that Y_n is equal in distribution to X_n for each n ≥ 0, and Y_n converges to Y₀ almost surely.^[11][12]
• If for all ε > 0,

  ${\displaystyle \sum _{n}\mathbb {P} \left(|X_{n}-X|>\varepsilon \right)<\infty ,}$

  then we say that X_n converges almost completely, or almost in probability towards X. When X_n converges almost completely towards X then it also converges almost surely to X. In other words, if X_n converges in probability to X sufficiently quickly (i.e. the above sequence of tail probabilities is summable for all ε > 0), then X_n also converges almost surely to X. This is a direct implication from the Borel–Cantelli lemma.

• If S_n is a sum of n real independent random variables:

  ${\displaystyle S_{n}=X_{1}+\cdots +X_{n}\,}$

  then S_n converges almost surely if and only if S_n converges in probability. The proof can be found in Page 126 (Theorem 5.3.4) of the book by Kai Lai Chung.^[13]

  However, for a sequence of mutually independent random variables, convergence in probability does not imply almost sure convergence.^{[14][circular reference]}

• The dominated convergence theorem gives sufficient conditions for almost sure convergence to imply L¹-convergence:

${\displaystyle \left.{\begin{matrix}X_{n}\xrightarrow {\overset {}{\text{a.s.}}} X\\|X_{n}|<Y\\\mathbb {E} [Y]<\infty \end{matrix}}\right\}\quad \Rightarrow \quad X_{n}\xrightarrow {L^{1}} X}$

5

• A necessary and sufficient condition for L¹ convergence is ${\displaystyle X_{n}{\xrightarrow {\overset {}{P}}}X}$ and the sequence (X_n) is uniformly integrable.
• If ${\displaystyle X_{n}\ \xrightarrow {\overset {}{p}} \ X}$, the followings are equivalent^[15]
  • ${\displaystyle X_{n}\ {\xrightarrow {\overset {}{L^{r}}}}\ X}$,
  • ${\displaystyle \mathbb {E} [|X_{n}|^{r}]\rightarrow \mathbb {E} [|X|^{r}]<\infty }$,
  • ${\displaystyle \{|X_{n}|^{r}\}}$ is uniformly integrable.

The Wikibook Econometric Theory has a page on the topic of: Convergence of random variables

• Proofs of convergence of random variables
• Convergence of measures
• Convergence in measure
• Continuous stochastic process: the question of continuity of a stochastic process is essentially a question of convergence, and many of the same concepts and relationships used above apply to the continuity question.
• Asymptotic distribution
• Big O in probability notation
• Skorokhod's representation theorem
• The Tweedie convergence theorem
• Slutsky's theorem
• Continuous mapping theorem