Bayesian Inference. Chapter 4: Regression and Hierarchical Models

Transcription

1 Bayesian Inference Chapter 4: Regression and Hierarchical Models Conchi Ausín and Mike Wiper Department of Statistics Universidad Carlos III de Madrid Advanced Statistics and Data Mining Summer School 29th June - 10th July, 2015 Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 1 / 34

2 Objective AFM Smith Dennis Lindley We analyze the Bayesian approach to fitting normal and generalized linear models and introduce the Bayesian hierarchical modeling approach. Also, we study the modeling and forecasting of time series. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 2 / 34

3 Contents 1 Normal linear models 1.1. ANOVA model 1.2. Simple linear regression model 2 Generalized linear models 3 Hierarchical models 4 Dynamic models Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 3 / 34

4 Normal linear models A normal linear model is of the following form: y = Xθ + ɛ, where y = (y 1,..., y n ) is the observed data, X is a known n k matrix, called the design matrix, θ = (θ 1,..., θ k ) is the parameter set and ɛ follows a multivariate normal distribution. Usually, it is assumed that: ( ɛ N 0 k, 1 ) φ I k. A simple example of normal linear model is the simple linear regression model ( ) T where X = and θ = (α, β) x 1 x 2... x T. n Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 4 / 34

5 Normal linear models Consider a normal linear model, y = Xθ + ɛ. A conjugate prior distribution is a normal-gamma distribution: θ φ N (m, 1φ ) V ( a φ G 2, b ). 2 Then, the posterior distribution given y is also a normal-gamma distribution with: m = ( X T X + V 1) 1 ( X T y + V 1 m ) V = ( X T X + V 1) 1 a = a + n b = b + y T y + m T V 1 m m T V 1 m Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 5 / 34

6 Normal linear models The posterior mean is given by: E [θ y] = ( X T X + V 1) 1 ( X T y + V 1 m ) = ( X T X + V 1) ( 1 X T X ( X T X ) ) 1 X T y + V 1 m = ( X T X + V 1) ( ) 1 X T Xˆθ + V 1 m where ˆθ = ( X T X ) 1 X T y is the maximum likelihood estimator. Thus, this expression may be interpreted as a weighted average of the prior estimator, m, and the MLE, ˆθ, with weights proportional to precisions since, conditional on φ, the prior variance is 1 φv and that the distribution of the MLE from the classical viewpoint is ˆθ ) φ N (θ, 1φ (XT X) 1 Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 6 / 34

7 Normal linear models Consider a normal linear model, y = Xθ + ɛ, and assume the limiting prior distribution, Then, we have that, θ y, φ N φ y G p(θ, φ) 1 φ. ( ˆθ, 1 ( X T X ) ) 1, φ n k 2, yt y ˆθ T ( X T X ) ˆθ 2. Note that ˆσ 2 = yt y ˆθ T (X T X)ˆθ n k is the usual classical estimator of σ 2 = 1 φ. In this case, Bayesian credible intervals, estimators etc. will coincide with their classical counterparts. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 7 / 34

8 ANOVA model The ANOVA model is an example of normal lineal model where: y ij = θ i + ɛ ij, where ɛ ij N (0, 1 φ ), for i = 1,..., k, and j = 1,..., n i. Thus, the parameters are θ = (θ 1,..., θ k ), the observed data are y = (y 11,..., y 1n1, y 21,..., y 2n2,..., y k1,..., y knk ) T, the design matrix is: X = n n Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 8 / 34

9 ANOVA model Assume conditionally independent normal priors, θ i N i = 1,..., k, and a gamma prior φ G( a 2, b 2 ). ( ) 1 m i, α i φ, for This corresponds to a normal-gamma ( prior distribution for (θ, φ) where 1 m = (m 1,..., m k ) and V = diag α 1,..., 1 α k ). Then, it is obtained that, and θ y, φ N φ y G n 1ȳ 1 +α 1m 1 n 1+α 1. n 1ȳ 1 +α 1m 1 n 1+α 1, 1 φ 1 α 1+n 1... ( a + n 2, b + k ni i=1 j=1 (y ij ȳ i ) 2 + k 2 1 α k +n k n i i=1 n i +α i (ȳ i m i ) 2 ) Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 9 / 34

10 ANOVA model If we assume alternatively the reference prior, p(θ, φ) 1 φ, we have: θ y, φ N ( n k φ G, 2 ȳ 1.. ȳ k, 1 φ (n k) ˆσ2 2 ), 1 n n k, where ˆσ 2 = 1 n k k i=1 (y ij ȳ i ) 2 is the classical variance estimate for this problem. A 95% posterior interval for θ 1 θ 2 is given by: 1 ȳ 1 ȳ 2 ± ˆσ + 1 t n k (0.975), n 1 n 2 which is equal to the usual, classical interval. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 10 / 34

11 Example: ANOVA model Suppose that an ecologist is interested in analysing how the masses of starlings (a type of birds) vary between four locations. A sample data of the weights of 10 starlings from each of the four locations can be downloaded from: Assume a Bayesian one-way ANOVA model for these data where a different mean is considered for each location and the variation in mass between different birds is described by a normal distribution with a common variance. Compare the results with those obtained with classical methods. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 11 / 34

12 Simple linear regression model Another example of normal linear model is the simple regression model: for i = 1,..., n, where ɛ i N Suppose that we use the limiting prior: y i = α + βx i + ɛ i, ( ) 0, 1 φ. p(α, β, φ) 1 φ. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 12 / 34

13 Simple linear regression model Then, we have that: ( α β y, φ N ˆαˆβ where: φ y G ), ( n 2 2, s y n 1 xi 2 i=1 φns x n x ( ) ) 1 r 2 2 n x n ˆα = ȳ ˆβ x, ˆβ = s xy s x, s x = n i=1 (x i x) 2, s y = n i=1 (y i ȳ) 2, s xy = n i=1 (x i x) (y i ȳ), r = s xy sx s y, ˆσ 2 = s ( ) y 1 r 2. n 2 Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 13 / 34

14 Simple linear regression model Thus, the marginal distributions of α and β are Student-t distributions: α ˆα ˆσ 2 n n i=1 x 2 i s x β ˆβ ˆσ 2 s x y t n 2 y t n 2 Therefore, for example, a 95% credible interval for β is given by: ˆβ ± equal to the usual classical interval. ˆσ sx t n 2 (0.975) Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 14 / 34

15 Simple linear regression model Suppose now that we wish to predict a future observation: Note that, y new = α + βx new + ɛ new. E [y new φ, y] = ˆα + ˆβx new V [y new φ, y] = 1 ( n i=1 x i 2 + nx 2 ) new 2n xx new + 1 φ ns x = 1 ( sx + n x 2 + nx 2 ) new 2n xx new + 1 φ ns x Therefore, y new φ, y N ( ˆα + ˆβx new, 1 φ ( ( x x new ) 2 s x + 1 n + 1 )) Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 15 / 34

16 Simple linear regression model And then, y new ˆα + ˆβx new ( ) y t n 2 ˆσ ( x xnew ) n + 1 s x leading to the following 95% credible interval for y new : ( ) ˆα + ˆβx new ± ˆσ ( x x new ) s x n + 1 t n 2 (0.975), which coincides with the usual, classical interval. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 16 / 34

17 Example: Simple linear regression model Consider the data file prostate.data that can be downloaded from: This includes, among other clinical measures, the level of prostate specific antigen in logs (lpsa) and the log cancer volume (lcavol) in 97 men who were about to receive a radical prostatectomy. Use a Bayesian linear regression model to predict the lpsa in terms of the lcavol. Compare the results with a classical linear regression fit. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 17 / 34

18 Generalized linear models The generalized linear model generalizes the normal linear model by allowing the possibility of non-normal error distributions and by allowing for a non-linear relationship between y and x. A generalized linear model is specified by two functions: 1 A conditional, exponential family density function of y given x, parameterized by a mean parameter, µ = µ(x) = E[Y x] and (possibly) a dispersion parameter, φ > 0, that is independent of x. 2 A (one-to-one) link function, g( ), which relates the mean, µ = µ(x) to the covariate vector, x, as g(µ) = xθ. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 18 / 34

19 Generalized linear models The following are generalized linear models with the canonical link function which is the natural parameterization to leave the exponential family distribution in canonical form. A logistic regression is often used for predicting the occurrence of an event given covariates: Y i p i Bin(n i, p i ) p i log = x i θ 1 p i A Poisson regression is used for predicting the number of events in a time period given covariates: Y i p i P(λ i ) log λ i = x i θ Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 19 / 34

20 Generalized linear models The Bayesian specification of a GLM is completed by defining (typically normal or normal gamma) prior distributions p(θ, φ) over the unknown model parameters. As with standard linear models, when improper priors are used, it is then important to check that these lead to valid posterior distributions. Clearly, these models will not have conjugate posterior distributions, but, usually, they are easily handled by Gibbs sampling. In particular, the posterior distributions from these models are usually log concave and are thus easily sampled via adaptive rejection sampling. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 20 / 34

21 Example: A logistic regression model The O-Ring data consist of 23 observations on Pre-Challenger Space Shuttle Launches On each launch, it is observed whether there is at least one O-ring failure, and the temperature at launch The goal is to model the probability of at least one O-ring failure as a function of temperature. Temperatures were 53, 57, 58, 63, 66, 67, 67, 67, 68, 69,70, 70, 70, 70, 72, 73, 75, 75, 76, 76, 78, 79, 81 Failures occurred at 53, 57, 58, 63, 70, 70, 75 Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 21 / 34

22 Hierarchical models Suppose we have data, x, and a likelihood function f (x θ) where the parameter values θ = (θ 1,..., θ k ) are judged to be exchangeable, that is, any permutation of them has the same distribution. In this situation, it makes sense to consider a multilevel modeling assuming a prior distribution, f (θ φ), which depends upon a further, unknown hyperparameter, φ, and use a hyperprior distribution, f (φ). In theory, this process could continue further, using hyperhyperprior distributions to estimate the hyperprior distributions. This is a method to elicit the optimal prior distributions. One alternative is to estimate the hyperparameter using classical methods, which is known as empirical Bayes. A point estimate ˆφ is then obtained to approximate the posterior distribution. However, the uncertainty in φ is ignored. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 22 / 34

23 Hierarchical models In most hierarchical models, the joint posterior distributions will not be analytically tractable as it will be, f (θ, φ x) f (x θ)f (θ φ)f (φ) However, often a Gibbs sampling approach can be implemented by sampling from the conditional posterior distributions: f (θ x, φ) f (x θ)f (θ φ) f (φ x, θ) f (θ φ)f (φ) It is important to check the propriety of the posterior distribution when improper hyperprior distributions are used. An alternative (as in for example Winbugs) is to use proper but high variance hyperprior distributions. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 23 / 34

24 Hierarchical models For example, a hierachical normal linear model is given by: ( x ij θ i, φ N θ i, 1 ), i = 1,..., n, j = 1,..., m. φ Assuming that the means, θ i, are exchangeable, we may consider the following prior distribution: ( θ i µ, ψ N µ, 1 ), ψ where the hyperparameters are µ y ψ. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 24 / 34

25 Example: A hierarchical one-way ANOVA Suppose that 5 individuals take 3 different IQ test developed by 3 different psychologists obtaining the following results: Test Test Test Then, we can assume that: ( X ij θ i, φ N θ i, 1 ), φ ( θ i µ, ψ N µ, 1 ), ψ for i = 1,..., 5, and j = 1, 2, 3, where θ i represents the true IQ of subject i and µ the mean true IQ in the population. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 25 / 34

26 Example: A hierarchical Poisson model The number of failures, X i at a power plant i is assumed to follow a Poisson distribution: X i λ i P(λ i t i ), para i = 1,..., 10, where λ i is the failure rate for pump i and t i is the length of operation time of the pump (in 1000s of hours). It seems natural to assume that the failure rates are exchangeable and thus we might assume: λ i γ E(γ), where γ is the prior hyperparameter. The observed data are: Pump t i x i Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 26 / 34

27 Dynamic models The univariate normal dynamic linear model (DLM) is: y t = F t θ t + ν t, ν t N (0, V t ) θ t = G t θ t 1 + ω t, ω t N (0, W t ). These models are linear state space models, where x t = F t θ t represents the signal, θ t is the state vector, F t is a regression vector and G t is a state matrix. The usual features of a time series such as trend and seasonality can be modeled within this format. If the matrices F t, G t, V t and W t are constants, the model is said to be time invariant. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 27 / 34

28 Dynamic models One of the simplest DLMs is the random walk plus noise model, also called first order polynomial model. It is used to model univariate observations and the state vector is unidimensional: y t = θ t + ν t, ν t N (0, V t ) θ t = θ t 1 + ω t, ω t N (0, W t ). This is a slowly varying level model where the observations fluctuate around a mean which varies according to a random walk. Assuming known variances, V t and W t, a straightforward Bayesian analysis can be carried out as follows. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 28 / 34

29 Dynamic models Suppose that the information at time t 1 is y t 1 = {y 1, y 2,..., y t 1 } and assume that: θ t 1 y t 1 N (m t 1, C t 1 ). Then, we have that: The prior distribution for θ t is: θ t y t 1 N (m t 1, R t ) where R t = C t 1 + W t The one step ahead predictive distribution for y t is: where Q t = R t + V t. y t y t 1 N (m t 1, Q t ) Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 29 / 34

30 Dynamic models The joint distribution of θ t and y t is: ( ) ( θt y t 1 mt 1 N m t 1 ( Rt, R t y t R t Q t )) The posterior distribution for θ t given y t = { y t 1, y t } is: θ t y t N(m t, C t ), m t = m t 1 + A t e t, A t = R t /Q t, e t = y t m t 1, C t = R t A 2 t Q t. where Note that e t is simply a prediction error term. The posterior mean formula could also be written as: m t = (1 A t ) m t 1 + A t y t. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 30 / 34

31 Example: First order polynomial DLM Assume a slowly varying level model for the water level in Lake Huron with known variances: V t = 1 and W t = 1. 1 Estimate the filtered values of the state vector based on the observations up to time t from f (θ t y t ). 2 Estimate the predicted values of the state vector based on the observations up to time t 1 from f (θ t y t 1 ). 3 Estimate the predicted values of the signal based on the observations up to time t 1 from f (y t y t 1 ). 4 Compare the results using e.g: Vt = 10 and W t = 1. Vt = 1 and W t = 10. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 31 / 34

32 Dynamic models When the variances are not known, the Bayesian inference for the system is more complex. One possibility is the use of MCMC algorithms which are usually based on the so-called forward filtering backward sampling algorithm. 1 The forward filtering step is the standard normal linear analysis to give f (θ t y t ) at each t, for t = 1,..., T. 2 The backward sampling step uses the Markov property and samples θ T from f (θ T y T ) and then, for t = T 1,..., 1, samples from f (θ t y t, θ t+1) Thus, a sample from the posterior parameter structure is generated. However, MCMC may be computationally very expensive for on-line estimation. One possible alternative is the use of particle filters. Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 32 / 34

33 Dynamic models Other examples of DLM are the following: A dynamic linear regression model is given by: y t = F t θ t + ν t, ν t N (0, V t ) θ t = θ t 1 + ω t, ω t N (0, W t ). The AR(p) model with time-varying coefficients takes the form: y t = θ 0t + θ 1t y t θ pt y t p + ν t, θ it = θ i,t 1 + ω it, This model can be expressed in state space form by setting θ = (θ 0t,..., θ pt ) and F = (1, y t 1,..., y t p). Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 33 / 34

34 Dynamic models The additive structure of the DLMs makes it easy to think of observed series as originating form the sum of different components,e.g., y t = y 1t +..., y h,t where y 1t might represent a trend component, y 2t a seasonal component, and so on. Then, each component, y it, might be described by a different DLM: y t = F it θ it + ν it, ν it N (0, V it ) θ it = G it θ t 1 + ω it, ω it N (0, W it ). By the assumption of independence of the components, y t is also a DLM described by: F t = (F 1t... F ht ), V t = V 1t V ht, and G t = G 1t... W 1t, W t =.... G ht W ht Conchi Ausín and Mike Wiper Regression and hierarchical models Advanced Statistics and Data Mining 34 / 34