Presentation on theme: "Sampling Research Questions Bruce D. Spencer Statistics Department and Institute for Policy Research Northwestern University SAMSI Workshop 10/21/10."— Presentation transcript:
Sampling Research Questions Bruce D. Spencer Statistics Department and Institute for Policy Research Northwestern University SAMSI Workshop 10/21/10
2 Introduction At the end of the opening workshop the group in Sampling, Modeling, and Inference raised a number of open questions related to sampling. Today I will discuss those questions, most of which are still unsolved.
3 Goal of Sample-Based Inference What is the target of the inference? –a stochastic model that generated a network or set of networks –population of networks, e.g., dynamic networks –multiple networks on a single population of edges –single network
4 Various Network Sampling Designs Conventional sample design to learn about the network –probabilities do not depend on observed data –E.g., Current Population Survey Adaptive sample design using the network –probabilities may depend on observed data –E.g. RDS; ego-centric samples; link-tracing designs Two-phase sampling to target further investigation of missing data or measurement error Subsampling (?) to reduce computational burden at possible loss of efficiency
5 Conventional Sampling Design to Learn about the Network(s) Samples of nodes or of edges - used for description of network(s) prediction of future state of network prediction of links/gaps/nodes fitting a model to the graph
6 Limitations from Sampling Sampling introduces random error into the estimates (and possibly bias, since E f(X) f (EX) for nonlinear f ) Sampling variance needs to be estimated, maybe bias does too; may be problematic for small samples Some population characteristics may not be estimable from a sample –E.g., maximum path length between any two nodes? –Number of components in a general graph? –What does estimable mean?
7 Limitations from Sampling If elements of interest (edges/non-edges, stars, motifs, etc.) have unequal probabilities of being observed, then –need to know the probabilities and adjust for them –or, need to have a model that explains the population –or, sometimes, both.
8 E.g.: Induced Graph Sampling Undirected parent graph (V, G) Sample nodes S V Observe G(S) G – observe edge/non-edge between u, v iff u,v S Conventional sampling with possibly unequal probabilities (including multiple- frame stratified multi-stage): probability of including u 1,u 2,...,u j and excluding u 1,u 2,...,v k knowable for any j, k Denote inclusion probabilities by
9 Horvitz-Thompson Estimators of Totals
10 H-T Estimators of Triad Distribution Define T k,u,v,w = 1 if u,v,w are distinct vertices sharing k edges and = 0 otherwise T k number of triads in E with 0 < k < 3 edges Other totals estimated similarly, e.g., number of stars or other motifs.
11 Degree Distribution d u degree of node u (its number of edges) M maximum degree in (E, G) N r number of nodes of degree 0 < r < M (F 0,F 1,…,F M ) is degree distribution, with F r =N r /N Degree distribution of the sample can differ from degree distribution of the population. Subnets of Scale-Free Networks are Not Scale- Free: Sampling Properties of Networks Stumpf, Wiuf, May (PNAS, 2005)
12 Estimation of Degree Distribution Induced subgraph from SRS of size n from (E,G) N r number of nodes of degree r in parent graph N r (S) number of nodes of degree r in subgraph
13 Estimation of Degree Distribution
14 Estimation of Mean and Variance of Degree Distribution
15 Partial Recap Using induced graph subsamples from conventional samples where joint inclusion probabilities are known, we can estimate –population values of descriptive statistics based on totals –degree distribution. (Only undirected graphs at one point in time discussed.) What about –other descriptive statistics –model fitting –large variances when sample size small –adaptive samples?
16 Approaches to Model Fitting 1.You trust* your model. Under certain conditions** on the sample design and the model, you can ignore the way the sample was selected and treat the sample as having been generated from the model. The sampling mechanism needs to be carefully examined to make sure it meets the requirements, which depend on the model being used. * Reagan and others, trust but verify ** Handcock and Gile (2010 AoAS) call the condition amenability and relate it to ignorability (Rubin 1976).
17 Approaches to Model Fitting 2.Model as descriptive statistic. You do not necessarily believe the model, but you want to fit the model the way you would if you completely observed the population. Anathema to many social scientists... E.g., in ERGMs, model fitting for population depends on sufficient statistics that are population totals. One can estimate them with H-T estimates (or alternatives) and then fit model. (Pavel Krivitsky poster) I have not investigated how to implement for other models. If both approaches are tried, large differences in fits can indicate model misspecification.
18 Adaptive Sampling Probabilities of observations depend on data from sampled units. Provides more information about network than conventional samples (Frank). Note: variances may be too large when sample is conventional but sparse. Probabilities of observing triads and larger typically unavailable, and even probabilities for dyads known for ego-centric designs but not link-tracing designs. (H-G 2010) In order to use full data, either need to estimate unknown probabilities (hard!!) or rely on model if amenability condition can be verified and model validated. E.g., when using conventional unequal probability samples to estimate a population total, the amenability condition typically does not hold.
19 Model Validation Model validation is important, but challenging when sampling probabilities are unknown. At the heart of every adaptive sample is a conventional sample. Use conventional sample to fit model as descriptive statistic. Compare result to model fitted under assumption of ignorability/amenability for (i) conventional sample and (ii) larger and more informative adaptive sample.
20 Recap What is the population (network, or set of networks) from which sample is selected? Sample design (and inference) to learn about the network –Static –Over time –Description of network –Prediction of future state of network and prediction of links/gaps/nodes
21 Recap Sample design (and inference) using the network to learn about a population –Respondent Driven Sampling –Adaptive Sampling –Others –Static and over time
22 Recap Subsampling design (and inference) to –Ease computational burden –Target further investigation to learn about measurement error When can inferences be made based on sample design information to provide approx. unbiasedness whether or not model is valid?
23 Recap How can model inferences be made? –What models? Exponential random graph models Mixed membership stochastic block models Latent space models Agent based models –What network characteristics (what summary statistics)
24 Recap What is effect of measurement error (and missing data, non-response) on inferences about network? –RDS samples –Others How to design and analyze randomized experiments when subjects are part of a static network? Dynamic? –Google experiments –Experiments on adolescents in schools (e.g., drug counseling, obesity treatment) – effects on peers