FREQUENTLY USED SYMBOLS AND ABBREVIATIONS

images
implies
images
implies and is implied by
images
converges to
images
increasing, decreasing
images
nonincreasing, nondecreasing
Γ(x)
gamma function
images
limit superior, limit inferior, limit
images, imagesn
real line, n-dimensional Euclidean space
images, imagesn
Borel σ-field on images, Borel σ-field on imagesn
IA
indicator function of set A
images(x)
= 1if x ≥ 0, and = 0 if x <0
μ
EX, expected value
mn
EXn, n ≥ 0 integral
βα
E|X|α,α> 0
μk
E(X — EX)k, k ≥ 0 integral
σ2
= μ2, variance
f′, f″, f″′
first, second, third derivative of f
~
distributed as
asymptotically (or approximately) equal to
images
convergence in law
images
convergence in probability
images
convergence almost surely
images
convergence in rth mean
RV
random variable
DF
distribution function
PDF
probability density function
PMF
probability mass function
PGF
probability generating function
MGF
moment generating function
d.f.
degrees of freedom
BLUE
best linear unbiased estimate
MLE
maximum likelihood estimate
MVUE
minimum variance unbiased estimate
UMA
uniformly most accurate
UMVUE
uniformly minimum variance unbiased estimate
UMAU
uniformly most accurate unbiased
MP
most powerful
UMP
uniformly most powerful
GLM
general linear model
i.o.
infinitely often
iid
independent, identically distributed
SD
standard deviation
SE
standard error
MLR
monotone likelihood ratio
MSE
mean square error
WLLN
weak law of large numbers
SLLN
strong law of large numbers
CLT
central limit theorem
SPRT
sequential probability ratio test
b(1,p)
Bernoulli with parameter p
b(n,p)
binomial with parameters n,p
NB(r;p)
negative binomial with parameters r,p
Ρ(λ)
Poisson with parameter λ
U[a,b]
uniform on [a, b]
G(α,β)
gamma with parameters α, β
B(α,β)
beta with parameters α, β
χ2 (n)
chi-square with d.f. n
images(μ, θ)
Cauchy with parameters μ, θ
images(μ,σ2)
normal with mean μ, variance σ2
t(n)
Student's t with n d.f.
F(m, n)
F-distribution with (m, n) d.f.
zα
100(1 - α)th percentile of images(0,1)
images,α
100(1 — α)th percentile of images2(n)
tn, α
100(1 — α)th percentile of t(n)
Fm,n,α
100(1 — α)th percentile of F(m, n)
AN images
asymptotically normal
GLR
generalized likelihood ratio
MRE
minimum risk equivariant
ℓnx
logarithm (to base e)of x
exp(X)
exponential
LMP
locally most powerful
images(x)
law or distribution of RV X
b(δ,.)
bias in estimator δ
iid
independent, identically distributed
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
18.222.184.200