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47 Cards in this Set
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- Back
- 3rd side (hint)
++ MAT 452 Topics
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++ MAT 452 Topics
1. [JointDistnTopics] 2. [MultivariateTopics] 3. [SamplingDistnsTopics] 4. [EstimatorTopics] |
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++ Joint Distn Topics
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++ Joint Distn Topics
1. [Joint Density Function] 2. [Joint Distribution Function] 3. [Joint Marginal Distn] 4. [Joint Conditional Distn] 5. [Joint Independence] 6. [Joint Expected Value] |
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Joint Density Function
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++ Joint Density Function
Properties of a Density Function: 1. f(y1,y2) >= 0 2. Intg[-∞,∞]Intg[-∞,∞]f(y1,y2)dy1dy2 = 1 |
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Joint Distribution Function Properties
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++ Joint Distribution Function Properties
Properties of a Distribution Function: 1. F(-∞,-∞) = F(-∞,y2) = F(y1,-∞) = 0 2. F(∞,∞) = 1 3. if y1' >= y1 and y2' >= y2 then F(y1',y2') - F(y1',y2) - F(y1,y2') + F(y1,y2) >= 0 4. F(y1,y2) = Intg[-∞,y1]Intg[-∞,y2]f(y1,y2)dy2dy1 |
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Joint Marginal Distn
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++ Joint Marginal Distn
1. f1(y1) = Intg[-∞,∞]f(y1,y2)dy2 |
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Joint Conditional Distn
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++ Joint Conditional Distn
1. f(y1|y2) = f(y1,y2)/f2(y2) |
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Joint Independence
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++ Joint Independence
1. f(y1,y2) = f1(y1)f2(y2) 2. f(y1,y2) = g(y1)h(y2) 3. E(g(y1)h(y2)) = E(g(y1))E(h(y2)) |
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Joint Expected Value
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++ Joint Expected Value
1. E(y1) = Intg[-∞,∞]Intg[-∞,∞]y1f(y1,y2)dy2dy1 2. E(y1) = Intg[-∞,∞]y1f1(y1)dy1 |
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++ MultivariateTopics
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++ MultivariateTopics
1. [Covariance] 2. [Linear Functions] 3. [Multinomial Probablity Distn] 4. [Bivariate Normal Distn] 5. [Joint Conditional Expectations] 6. [MethodsOfFindingDistns] 7. [Order Statistics] |
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Covariance
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1. Cov(Y1,Y2) = E((Y1-μ1)(Y2-μ2))
2. Cov(Y1,Y2) = E(Y1Y2) - μ1μ2 3. ρ = Cov(Y1,Y2)/σ1σ2 4. If indp. then Cov(Y1,Y2) = 0 |
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Linear Functions
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++ Linear Functions
1. U = Σ(i=1,n)aiYi 2. E(U) = Σ(i=1,n)aiE(Yi) 3. V(U) = Σ(i=1,n)ai^2V(Yi) + 2ΣΣ(i<j)aiajCov(Yi,Yj) 4. Cov(U1,U2) = Σ(i=1,n)Σ(j=1,m)aiajCov(Yi,Yj) ??? |
None
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Multinomial Probablity Distn
p(y1,,yk) = |
++ Multinomial Probablity Distn
1. p(y1,..yk) = n!(p1^y1)(p2^y2)...(pk^yk)/y1!y2!...yk! OR p(y1,..yk) = n!Πpk^yk/Πyk! 2. Σ(i=1,k)yi=n 3. Requires [Multinomial Experiment] assumptions |
Advanced Distributions
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Multinomial Experiment
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++ Multinomial Experiment
1. n identical trials 2. k classes of outcomes 3. p1+p2...+pk=1 4. independent 5. Yi = number of trials for outcome class i |
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Bivariate Normal Distn
1. f(y1,y2) = ? 2. z1 = ? 3. Q = ? |
++ Bivariate Normal Distn
1. f(y1,y2) = e^(-Q/2)/(2πσ1σ2√(1-ρ^2)) 2. z1 = (y1-μ1)/σ1 3. Q = (z1^2-2ρz1z2+z2^2)/(1-ρ^2) |
f(y)
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Joint Conditional Expectations
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++ Joint Conditional Expectations
1. E(Y1|Y2=y2) = Intg[-∞,∞]y1f(y1|y2)dy1 2. E(Y1) = E(E(Y1|Y2) 3. V(Y1) = E(V(Y1|Y2)) + V(E(Y1|Y2)) |
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++ Methods Of Finding Distns
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++ Methods Of Finding Distns
1. [Method of Distribution Functions] 2. [Method of Transformations] 3. [Method of MGFs] |
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Method of Distribution Functions
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++ Method of Distribution Functions
1. U = h(y) 2. Fu(u) = P(U<=u) = P(h(y)<=u) = P(Y<=h-1(u)) = Intg[-∞,h-1(u)]f(y)dy 3. fu(u) = dFu(u)/du |
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Method of Transformations
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++ Method of Transformations
1. U = h(y) 2. dh-1/du = d(h-1(u))/du 3. fu(u) = fy(h-1(u))|dh-1/du| |
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Method of MGFs
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++ Method of MGFs
1. mu(t) = E(e^tU) = Intg[-∞,∞](e^tU)f(u)du See also: 2. [Indpendent MGFs] 3. [Independent Normal MGFs] |
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Indpendent MGFs
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++ Indpendent MGFs
1. if Y1,Y2,...,Yn indp 2. U = Y1 + Y2 + ... + Yn 3. mu(t) = my1(t)my2(t)...myn(t) |
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++ Independent Normal MGFs
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++ Independent Normal MGFs
1. U = Σ(i=1,n)aiYi = a1Y1 + a2Y2 + ... + anYn 2. E(U) = Σ(i=1,k)aiE(Yi) 3. V(U) = Σ(i=1,k)(ai^2)(σi^2) |
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Order Statistics
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++ Order Statistics
1. Y(1) = min(Y1,...,Yn) 2. g(1)(y) = n(1-F(y))^(n-1)f(y) 3. Y(n) = max(Y1,...,Yn) 4. g(n)(y) = n(F(y))^(n-1)f(y) 5. g(k)(yk) = ? 6. g(j)(k)(yj,yk) = ? |
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++ Sampling Distns Topics
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++ Sampling Distns Topics
1. [YbDistn] 2. [SumOfZSquaresDistn] 3. [SampleVarianceDistn] 4. [TDistn] 5. [FDistn] 6. [CentralLimitTheorem] |
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++ Yb Distn
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++ Yb Distn
1. Yb = (1/n)Σ(i=1,n)Yi 2. μyb = μ 3. σyb^2 = σ^2/n 4. Zyb = √n((Yb-μ)/σ) |
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++ Sum Of Z Squares Distn
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++ Sum Of Z Squares Distn
1. Zi = (Yi-μ)/σ 2. Σ(i=1,n)Zi^2 3. ~ X2(n) distn 4. ? proof ? |
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++ S^2 Distn
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++ S^2 Distn
1. S^2 = (1/(n-1))Σ(i=1,n)(Yi - Yb)^2 2. W = (n-1)(S^2)/σ^2 = (1/σ^2)Σ(i=1,n)(Yi - Yb)^2 3. ~ X2(n-1) distn 4. ? proof (n-1) ? 5. Yb and S^2 are independent 6. used to make inference about the population variance |
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++ Students-t Distn
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++ Students-t Distn
1. Z ~ N(0,1) 2. W ~ X2(ν df) 3. T = Z/√(W/ν) 4. ~ t(ν df) 5. show T = √n(Yb - μ)/S ~ t(ν=n-1) 6. used to make inferences about the population expected value |
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++ Fdistn
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++ Fdistn
1. W1,W2 ~ X2(ν df) 2. F = (W1/ν1)/(W2/ν2) 3. ~ F(ν1,v2 df) 4. used for determining magnitude of rations of estimates of variance |
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++ Central Limit Central Limit Theorem
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++ Central Limit Theorem
1. E(Yi) = μ 2. V(Yi) = σ^2 3. Un = √n((Yb-μ)/σ) 4. ~ converges to N(0,1) as n->∞ 5. ProofOfCentralLimitTheorem 6. NormalBinomial |
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++ Pivot Method CI
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++ Pivot Method CI
1. Pivot function has θ as only unknown property, but it's probability distribution does not depend on θ. 2. P(θL^ < θ < θU^) = 1 - α 3. P(a < U < b) = 1 - α 4. U = f(θY) |
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++ Proof Of Central Limit Theorem
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++ Proof Of Central Limit Theorem
1. transform W into Zi = (Yi-μ)/σ with μ=E(Zi)=0, σ=E(Zi^2)=1 2. Un = Σ(i=1,n)Zi/√n 3. mzi(t/√n) = E(e^tW/√n) = 1 + 0(t/(1!√n)) + 1((t/√n)^2/2!) + E(Zi^3)((t/√n)^3/3!) + ... 4. mn(t) = Π(i=1,n)mzi(t/√n) = (1 + t^2/2n + ...)^n 5. ln(mn(t)) = n*ln(1 + t^2/2n + ...) 6. ln(mn(t)) = n((t^2/2n + ...) - (t^2/2n + ...)^2/2 + ...) 7. lim(n->∞)mn(t) = e^(t^2/2) |
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Normal Approximation to the Binomial Distribution
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1. U = Y/n = (1/n)Σ[i=1,n]Xi
1. μy = np 2. σy^2 = npq 3. n > 9(max(p,q)/min(p,q)) |
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++ Large Sample CI
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++ Large Sample CI
1. Use normal distribution (Z) to determine confidence interval where n>100? 2. Z = (θ^-θ)/σθ^ 3. P(-zα/2 < Z < zα/2) = 1-α 4. θ = θ^+-(zα/2)(σθ^) |
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++ Bias
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++ Bias
1. B(θ^) = E(θ^)-θ 2. MSE(θ^) = E((θ^-θ)^2) = V(θ^) + B(θ^)^2 |
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++ Sample Size
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++ Sample Size
1. ε=(zα/2)(σθ^) 2. since σθ^ is a function of n, substitute and solve for n |
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++ Small Sample CI
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++ Small Sample CI
1. T = (Yb-μ)/(S/√n) 2. S^2 = (1/n-1)Σ(Yi-Yb)^2 3. μ = Yb+-(tα/2)(S/√n) 4. ν = n-1 df 5. |μ1-μ2| = |Yb1-Yb2|+-(tα/2)(Sp√(1/n1+1/n2) 6. Sp = ((n1-1)S1^2+(n2-1)S2^2)/(n1+n2-2)) 7. ν = n1+n2-2 df |
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++ Var CI
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++ Var CI
1. σ^2L = (n-1)(S^2)/(X2(α/2)) 2. σ^2U = (n-1)(S^2)/(X2(1-α/2)) 3. Pivot ~ X2(n-1 df) |
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++ Common Estimators
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++ Common Estimators
1. [Estimator_μ] 2. [Estimator_p] 3. [Estimator_μ1-μ2] 4. [Estimator_p1-p2] |
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++ Estimator Topics
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++ Estimator Topics
1. [CommonEstimators] 2. [PivotMethodCI] 3. [VarCI] 4. [PropertiesOfEstimatorTopics] 5. Mehod of Moments 6. Method of Max Likelihood |
Topics
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++ Estimator_μ
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++ Estimator_μ
1. θ = μ 2. θ^ = Yb 3. σθ^ = σ/√n |
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++ Estimator_p
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++ Estimator_p
1. θ = p 2. θ^ = p^ = Y/n 3. σθ^ = √pq/n |
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++ Estimator_μ1-μ2
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++ Estimator_μ1-μ2
1. θ = μ1-μ2 2. θ^ = Yb1-Yb2 3. σθ^ = √(σ1^2/n1 + σ2^2/n2) |
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++ Estimator_p1-p2
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++ Estimator_p1-p2
1. θ = p1-p2 2. θ^ = p1^-p2^ 3. σθ^ = √(p1q1/n1 + p2q2/n2) |
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++ Properties Of Estimators
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++ Properties Of Estimators
1. [Unbiasedness] 2. [Efficiency] 3. [Consistency] 4. [Sufficiency] |
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++ Efficiency
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++ Efficiency
1. eff(θ1^,θ2^) = V(θ2^)/V(θ1^) |
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++ Consistency
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++ Consistency
1. lim(n->∞)V(θn^)=0 2. [ConsistencyProof] 3. Un converges ~N(0,1), Wn converges 1, lim(n->∞)Un/Wn ~N(0,1) |
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++ Sufficiency
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++ Sufficiency
1. P(x1,...,xn|Y=y)=P(x1,...,xn,y)/P(y) = U 2. Y is sufficient for θ if U does not depend on θ 3. FactorizationCriterion 4. [MVUE] |
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