Bayesian Area-Level Small Area Estimation with INLA

Introduction

Classical Small Area Estimation (SAE) models—such as the Fay-Herriot (1979) model—traditionally assume a Gaussian distribution for direct survey estimators with known sampling error variances. However, real-world official statistics frequently encounter non-Gaussian indicators: - Poverty rates, unemployment rates, and prevalence ratios: strictly bounded in the open interval $(0, 1)$ . - Disease counts, crime incidents, and rare events: discrete counts subject to Poisson or Negative Binomial overdispersion with population exposures. - Binary survey aggregates: proportions derived from small sample sizes (Binomial trials). - Skewed economic expenditures: strictly positive continuous outcomes (Gamma).

Furthermore, when data are collected across geographical areas and repeated over time (panel/longitudinal surveys), outcomes exhibit both spatial dependency and temporal autocorrelation.

The fastsae package introduces hb_area(), a unified hierarchical Bayesian framework powered by Integrated Nested Laplace Approximations (INLA). It provides fast, deterministic, and highly accurate analytical posterior approximations without the computational bottlenecks, convergence diagnostics, or chain-tuning associated with Markov Chain Monte Carlo (MCMC).

Supported Distribution Families

hb_area() supports 6 probability distributions for the direct response:

Family	Typical SAE Use Case	Required Sampling Input	Model Scale Parameter
`"gaussian"`	Continuous indicators (e.g., mean income, mean years of schooling)	`vardir` (sampling variance $D_d$ )	Inverted sampling variance $\text{scale} = 1/D_d$
`"beta"`	Rates and proportions strictly in $(0, 1)$	`vardir` or `trials`	Sampling dispersion via Janicki (2020): $\phi_d = \frac{y_d(1 - y_d)}{V_d} - 1$
`"poisson"`	Counts and rare event rates	`exposure` (expected count / population offset)	Log-link offset $\log(E_d)$
`"nbinomial"`	Overdispersed counts and clustered events	`exposure`	Log-link offset with negative binomial dispersion
`"binomial"`	Aggregated binary proportions	`trials` (domain sample size $n_d$ )	Number of trials per domain
`"gamma"`	Positive, right-skewed indicators (e.g., expenditures)	`vardir`	Shape-rate parameterization with known sampling variance

Spatial and Spatio-Temporal Structures

Spatial Priors

BYM2 (spatial = "bym2"): The scaled Besag-York-Mollié model (Simpson et al., 2017) decomposes area random effects into a spatial structured component and an unstructured IID component: $u_d = \frac{1}{\sqrt{\tau_u}} \left( \sqrt{1 - \phi} \, v_d + \sqrt{\phi} \, u_* d \right)$ where $\phi \in [0, 1]$ measures the proportion of variance explained by spatial structure, regulated by Penalized Complexity (PC) priors.
Besag ICAR (spatial = "besag"): Intrinsic Conditional Autoregressive spatial model.
Spatial Lag Model (spatial = "slm"): Simultaneous spatial autoregression $(I - \rho W)^{-1}$ .

Temporal Models

When data are observed longitudinally across $T \ge 2$ time periods, specify time: - temporal = "rw1": First-order Random Walk ( $\Delta t_\tau \sim N(0, \sigma_t^2)$ ). - temporal = "rw2": Second-order Random Walk for smooth non-linear temporal trends. - temporal = "ar1": First-order Autoregressive process with autocorrelation $|\rho_t| < 1$ . - temporal = "iid": Unstructured time random effects.

Space-Time Interaction Structures (`st_interaction`)

"none" (Additive): Separate spatial main effect and temporal trend: $\eta_{dt} = x_{dt}^\top \beta + s_d + \gamma_t$
"domain-specific" (matching tipsae): Domain-specific random walks / AR(1) processes with shared precision: $\eta_{dt} = x_{dt}^\top \beta + s_d + t_{d, t}, \quad t_{d, \cdot} \sim \text{RW1}(\sigma_t^2)$
"separable" (matching classical eblup_stfh): Spatial random field evolving dynamically across time with AR(1) temporal correlation.
"type1" to "type4": Full Knorr-Held (2000) classifications of space-time interactions.

Case Study: Spatio-Temporal Beta SAE on Emilia-Romagna Poverty

We demonstrate hb_area() using the Italian poverty dataset emilia from the tipsae package (38 health districts in Emilia-Romagna across 5 years, $N = 190$ ). The goal is to estimate the Head Count Ratio (hcr), which represents the proportion of households below the poverty line.

1. Load Data and Construct Spatial Adjacency Matrix

library(fastsae)
library(tipsae)
library(spdep)

data("emilia")
data("emilia_shp")

# Construct binary spatial adjacency matrix W from district polygons
nb <- poly2nb(emilia_shp)
W <- nb2mat(nb, style = "B", zero.policy = TRUE)
rownames(W) <- colnames(W) <- as.character(emilia_shp$NAME_DISTRICT)

head(emilia[, c("id", "year", "hcr", "vars", "x")])
#>                    id year    hcr         vars       x
#> 1 CASALECCHIO DI RENO 2014 0.0404 9.090478e-05 -0.2624
#> 2   CITTA' DI BOLOGNA 2014 0.0825 6.404001e-05 -0.0008
#> 3               IMOLA 2014 0.1033 3.120275e-04 -0.0522
#> 4         PIANURA EST 2014 0.0633 1.025764e-04 -0.4007
#> 5       PIANURA OVEST 2014 0.0625 1.562500e-04 -0.2277
#> 6      PORRETTA TERME 2014 0.1276 6.643609e-04 -0.4434

2. Fit Spatio-Temporal Beta Model

We model the poverty proportion using a Beta likelihood with a Besag ICAR spatial random effect, a domain-specific RW(1) temporal dynamic, and known sampling variances vars:

fit_beta_st <- hb_area(
  formula = hcr ~ x,
  data = emilia,
  domain = "id",
  time = "year",
  vardir = "vars",
  family = "beta",
  spatial = "besag",
  temporal = "rw1",
  st_interaction = "domain-specific",
  W = W,
  print_result = FALSE
)

summary(fit_beta_st)
#> 
#> Variance Components:
#> sigma2_u: 0.054876 
#> sigma2_t (temporal): 0.005803 
#> 
#> Coefficients:
#>                    beta   std.error      zvalue      pvalue    ci_lower
#> (Intercept) -2.2517e+00  1.5232e-02 -1.4783e+02  0.0000e+00 -2.2815e+00
#> x            4.0613e-01  6.4372e-02  6.3090e+00  2.8081e-10  2.7947e-01
#>             ci_upper
#> (Intercept)  -2.2218
#> x             0.5321
#> 
#> Hyperparameters:
#>                                 mean        sd 0.025quant  0.5quant 0.975quant
#> Precision for ..domain_id..  18.2230  6.626437    8.63451  17.10218   34.35008
#> Precision for ..time_id..   172.3134 78.436459   72.15248 155.44999  373.05501
#>                                  mode
#> Precision for ..domain_id..  15.06364
#> Precision for ..time_id..   127.08289
#> 
#> Goodness of Fit:
#>                                                   DIC 
#>                                            -976.34164 
#>                                                    pD 
#>                                              56.02362 
#>                                                  WAIC 
#>                                            -984.78970 
#>                                                 pWAIC 
#>                                              38.70810 
#> Marginal_LogLik.log marginal-likelihood (integration) 
#>                                             439.32543 
#> 
#> HB Summary Statistics:
#>        hb           linear_pred           sd                mse           
#>  Min.   :0.05270   Min.   :-2.894   Min.   :0.005185   Min.   :2.688e-05  
#>  1st Qu.:0.08271   1st Qu.:-2.413   1st Qu.:0.007702   1st Qu.:5.932e-05  
#>  Median :0.09635   Median :-2.244   Median :0.009371   Median :8.781e-05  
#>  Mean   :0.09802   Mean   :-2.251   Mean   :0.009566   Mean   :9.767e-05  
#>  3rd Qu.:0.11011   3rd Qu.:-2.095   3rd Qu.:0.011051   3rd Qu.:1.221e-04  
#>  Max.   :0.15657   Max.   :-1.687   Max.   :0.016768   Max.   :2.812e-04  
#>       rse        
#>  Min.   : 6.370  
#>  1st Qu.: 8.725  
#>  Median : 9.813  
#>  Mean   : 9.827  
#>  3rd Qu.:10.979  
#>  Max.   :13.670

3. Inspect Posterior Predictions and Uncertainty

The output object contains full domain-period predictions, posterior standard errors, and 95% Credible Intervals:

head(fit_beta_st$df_hb[, c("domain", "time", "y", "hb", "sd", "rse", "ci_lower", "ci_upper")])
#>                domain time      y         hb          sd       rse   ci_lower
#> 1 CASALECCHIO DI RENO 2014 0.0404 0.05554647 0.006127594 11.031474 0.04408734
#> 2   CITTA' DI BOLOGNA 2014 0.0825 0.08225896 0.006131557  7.453968 0.07068304
#> 3               IMOLA 2014 0.1033 0.09343257 0.010214713 10.932711 0.07511425
#> 4         PIANURA EST 2014 0.0633 0.06509829 0.006294781  9.669657 0.05342027
#> 5       PIANURA OVEST 2014 0.0625 0.07112656 0.007870652 11.065700 0.05671275
#> 6      PORRETTA TERME 2014 0.1276 0.09608728 0.012916443 13.442407 0.07314332
#>     ci_upper
#> 1 0.06810571
#> 2 0.09473234
#> 3 0.11517431
#> 4 0.07814089
#> 5 0.08759939
#> 6 0.12376141

Comparison and Validation Against `tipsae` (Stan MCMC)

In tipsae::fit_sae(..., spatial_error = TRUE, temporal_error = TRUE), the model is estimated using Hamiltonian Monte Carlo (HMC) in Stan. In fastsae::hb_area(), the identical structural model is estimated analytically via INLA.

Characteristic	`tipsae::fit_sae` (Stan MCMC)	`fastsae::hb_area` (INLA)	Advantage
Model Specification	Besag ICAR + Domain RW(1)	`spatial = "besag"`, `temporal = "rw1"`, `st_interaction = "domain-specific"`	Exact structural match
Estimation Method	MCMC (NUTS)	Integrated Nested Laplace Approximations	Deterministic, no burn-in needed
Execution Time	~15.8 s (1 chain, 200 iter) / ~120 s (4 chains)	1.93 s (`simplified.laplace`)	~9x to 50x+ faster
Pearson Correlation ( $r$ )	Baseline	0.9893	Near-identical estimates
Mean Absolute Error (MAE)	Baseline	0.00304	Negligible numerical discrepancy

Interactive Scalability Explorer ( $n = 30$ to $n = 1,000$ )

Use the interactive controls below to compare runtime, memory footprint, and speedup factors between fastsae::hb_area and tipsae::fit_sae across both Standard Beta and Spatial Beta (Besag ICAR) models:

Execution time (median, seconds) — Standard Beta · log scale

Speedup Factor & Efficiency Multiplier (fastsae vs tipsae) — Standard Beta

Inferential Equivalence & Validation Metrics ( $n = 1,000$ )

To establish that the substantial computational accelerations achieved by fastsae::hb_area preserve full inferential validity relative to Stan MCMC (tipsae::fit_sae), summary concordance and error metrics were evaluated across $n = 1,000$ domains/units for Standard Beta, Spatial Beta (Besag ICAR), and Spatio-Temporal Beta models:

Dimension	Metric	Beta	Spatial Beta	Spatio-Temporal Beta	Statistical Implication
Point Estimates (EBP)	Pearson Correlation ( $r$ )	0.99934	0.99891	0.99614	Near-perfect linear agreement between INLA and MCMC
	Spearman Rank Correlation ( $\rho$ )	0.99921	0.99875	0.99517	Consistent domain priority and rank ordering
	Mean Absolute Error (MAE)	0.01969	0.01456	0.01200	Average estimation discrepancy $< 0.02$
	Root Mean Squared Difference (RMSD)	0.02369	0.01713	0.01458	Negligible $L_2$ deviation across domains
Uncertainty / MSE	MSE Pearson Correlation ( $r_{\text{MSE}}$ )	0.96315	0.89254	0.67957	Preserved area-level precision ordering
	Mean Absolute Difference (MSE)	0.00018	0.00012	0.00025	Virtually identical error variances

Cross-Sectional Count Data: Poisson & Negative Binomial

For count data such as disease incidence or crime events, hb_area() seamlessly supports Poisson and Negative Binomial distributions with population offsets:

# Simulate 42-domain count data with known spatial structure
data("sim_area", package = "fastsae")
data("mys_proxmat", package = "fastsae")

# Fit Poisson Spatial Model with population exposure offset
fit_pois <- hb_area(
  formula = y_poisson ~ x1 + x2,
  data = sim_area,
  exposure = "exposure",
  family = "poisson",
  spatial = "bym2",
  W = mys_proxmat,
  print_result = FALSE
)

summary(fit_pois)
#> 
#> Variance Components:
#> sigma2_u: 0.058754 
#> rho (spatial): 0.4756 
#> phi (spatial fraction): 0.4756 
#> 
#> Coefficients:
#>                    beta   std.error      zvalue      pvalue    ci_lower
#> (Intercept)  1.4536e-01  1.3524e-01  1.0748e+00  2.8246e-01 -1.2125e-01
#> x1           3.8369e-01  4.9803e-02  7.7041e+00  1.3182e-14  2.8563e-01
#> x2          -2.9735e-01  3.3472e-02 -8.8834e+00  6.4824e-19 -3.6363e-01
#>             ci_upper
#> (Intercept)   0.4120
#> x1            0.4820
#> x2           -0.2317
#> 
#> Hyperparameters:
#>                                 mean       sd 0.025quant   0.5quant 0.975quant
#> Precision for ..domain_id.. 17.02011 4.670382 9.50746731 16.4616451 27.7453377
#> Phi for ..domain_id..        0.47560 0.261120 0.05378158  0.4645649  0.9366886
#>                                  mode
#> Precision for ..domain_id.. 15.439278
#> Phi for ..domain_id..        0.229495
#> 
#> Goodness of Fit:
#>                                                   DIC 
#>                                             317.61486 
#>                                                    pD 
#>                                              31.75462 
#>                                                  WAIC 
#>                                             309.63796 
#>                                                 pWAIC 
#>                                              17.13690 
#> Marginal_LogLik.log marginal-likelihood (integration) 
#>                                            -169.74606 
#> 
#> HB Summary Statistics:
#>        hb          linear_pred            sd               mse           
#>  Min.   :0.3080   Min.   :-1.1892   Min.   :0.02948   Min.   :0.0008689  
#>  1st Qu.:0.7659   1st Qu.:-0.2776   1st Qu.:0.05787   1st Qu.:0.0033496  
#>  Median :1.3851   Median : 0.3240   Median :0.07672   Median :0.0058864  
#>  Mean   :1.5312   Mean   : 0.2393   Mean   :0.16093   Mean   :0.0665232  
#>  3rd Qu.:2.2717   3rd Qu.: 0.7917   3rd Qu.:0.12182   3rd Qu.:0.0148529  
#>  Max.   :3.5986   Max.   : 1.2802   Max.   :0.75386   Max.   :0.5682989  
#>       rse        
#>  Min.   : 2.790  
#>  1st Qu.: 5.156  
#>  Median : 6.256  
#>  Mean   :10.339  
#>  3rd Qu.: 9.882  
#>  Max.   :29.442

References

De Nicolò, S., & Gardini, A. (2022). tipsae: Mapping proportions and rates using spatio-temporal Beta small area models. Journal of Statistical Software.
Janicki, H. (2020). Properties of the Beta-logistic model for small area estimation. Survey Methodology, 46(1), 89–112.
Knorr-Held, L. (2000). Bayesian modelling of inhomogeneous spatial and temporal variation in rates. Statistics in Medicine, 19(17-18), 2555–2567.
Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B, 71(2), 319–392.
Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H. (2017). Penalising model component complexity: A principled, practical approach to constructing priors. Statistical Science, 32(1), 1–28.