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STAT 5060 Advanced Modeling

创建日期：2024-04-19 16:08:04

所属分类：统计学statistics

标签： STAT 5060

Advanced Modeling

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STAT 5060 Advanced Modeling and Data Analysis

Assignment
1. Consider a GLM with count data as follows: for i = 1, · · · , n,
yi ∼ Poisson(µi), log(µi) = xTi β, (1)
where β = (−1, 1, 1, 1)T , xi = (1, xi1, xi2, xi3)T , xi1 ∼ U(0, 1), and
(xit2, xit3) ∼ N(µ,Σ) with µ = (0, 0)T and Σ =
[
1 0.2
0.2 1
]
.
(a) Generate data using the above setting with sample size n = 500.
(b) Estimate β based on Model (1) and the generated data.
(c) Repeat steps (a) and (b) for 10 times and calculate the Bias and
RMS of the parameter estimates.
[Hint: (i) Bias of βˆ is given by ( 1S
∑S
j=1 βˆj) − β0, where β0 is the true
value of β, βˆj is the estimate of β at the jth replication, S is the number
of replications; RMSE of βˆ is given by
[
1
S
∑S
j=1(βˆj − β0)2
] 1
2 . (ii) The R
packages and the corresponding functions are marked in red. (iii) In this
problem, use the stats package, via the glm.fit function ]
2. Consider an extended GLM with nominal data as follows: for i = 1, · · · , n,
yi ∼ Categorical(pii1, · · · , pii4), piij = P (yi = j),
log
piij
pii4
= xTi βj , j = 1, 2, 3,
(2)
where β1 = (−1,−1, 1)T , β2 = (−1,−1, 1)T , β3 = (1,−1, 1)T , and xi =
(1, xi1, xi2)
T with xi1 ∼ U(0, 1) and xi2 ∼ N(0, 1).
1
(a) Generate data using the above setting with sample size n = 800.
(b) Estimate β based on Model (2) and the generated data.
(c) Repeat steps (a) and (b) for 10 times and calculate the Bias and
RMS of the parameter estimates.
[Hint: the nnet package, via the multinom function]
3. Consider a GLM with longitudinal binary data as follows: for i = 1, · · · , n,
t = 1, · · · , T ,
yit ∼ Bernoulli(piit), logit(piit) = xTitβ + ui, (3)
where β is a vector of regression coefficients, xit = (1, xit1, xit2)
T , xit1 ∼
Bernoulli(0.5), xit2 ∼ N(0, 1), ui is a subject-specific random effect, and
ui ∼ N(0, σ2). The true values of the parameters are β = (−1,−0.5,−0.5)T
and σ2 = 1.
(a) Generate data using the above setting with n = 500 and T = 5.
(b) Estimate β and σ2 based on Model (3) and the generated data.
(c) Repeat steps (a) and (b) for 10 times and calculate the Bias and
RMSE of the parameter estimates.
4. Reanalyze Example 3.3 using cumulative logit models with and without
random intercept ui and compare the results obtained from these two
competing models.
[Hint: the ordinal package, via the clmm and clmm2 functions; the
mixor package, via the mixor function; the MCMCglmm package, via
family=“ordinal”; the brms package, e.g. via family=“cumulative”]

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239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 MGMTMFE 405 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