A Statistical Framework for Analysing Big Data Global Conference on Big Data for Official Statistics October, 2015 by S Tam, Chief
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1 A Statistical Framework for Analysing Big Data Global Conference on Big Data for Official Statistics October, 2015 by S Tam, Chief Methodologist Australian Bureau of Statistics 1
2 Big Data (BD) Issues Characterise BD with big N, big p and big t Sampling error Reduced by increasing the sample size Bias Coverage bias - Big Data population is not the population of interest Self selection bias are their views representative of the silent population segments? Representation bias multiple representation Measurement error Are the data related to the concept of interest? Increasing the sample size does NOT reduce nonsampling errors 2
3 Methods overview Domain (e.g. crop) modelling Machine learning methods Decision Trees Artificial Neural Networks Support Vector Machines Nearest Neighbour Ensemble classifiers Statistical modelling Spatial-Temporal models Use space and time information Use in ABS modelling for satellite imagery and simulated phone data
4 ABS method overview Two-Step Approach Calibration and Prediction Use a sample to calibrate the Big Data (treated as covariates ) using ground truths/measurements Calibrate using a linear model with time varying coefficients Dynamic Model Estimate parameters (using Frequentist/Bayesian approaches) Predict the non-sampled values using the covariates
5 Selectivity - Missing Covariate challenges The selectivity bias issue When the Big Data population only intersects with the inference population, Big Data covariates are missing Two problems Bias in estimating beta Covariates are missing Can we just ignore the selectivity bias issue? Big Data bias and statistical modelling
6 When can selectivity be ignored? Inference on finite population values Modelling the missing processes Sampling and missing data processes can be ignored if these processes are not dependent on the unobserved population values Fulfilled if the training data set is selected by probability sampling There is no missing data or the missing data is missing at random Big Data process can be ignored if this process is not dependent on the missing covariates This condition is difficult to check If condition not satisfied, modelling for the missing covariates a difficult task will be needed.
7 Predicting crop types from Satellite Imagery Satellite imagery Data at time = t Big Data - reflectance data from 7 frequency bands from satellite imagery - Statistical models require ground truths/measurements 7
8 Some interesting observations Satellite Imagery no undercoverage (missing covariates) issues Enhanced vegetation index EVI plotted over the growing season
9 What covariates to use? This is where crop science comes in Possible covariate curves Land surface temperature curve Moisture curve Grow curves etc. Want to use the whole curve Need a method to pick finite points Functional Data Analysis Pick the points which explains most of the variation of the curve» Functional Data Analysis
10 Data, Static Models and Accuracy The training data Models and accuracy
11 Temporal Modelling Data over time Dynamic models
12 The Algorithms Dynamin Linear Model Dynamic Logistic Regression Model
13 Concluding remarks These methods apply to a large number of Big Data applications For example, mobile phone data The challenge (cost, feasibility etc.) is availability of ground truths/measurements For official statistics, there is a role for survey sampling even with Big Data
14 References: Tam, S & Clarke, F 2015 (2015). Big Data, Official Statistics and Some Initiatives of the ABS, International Statistical Review, to appear. Tam, S (2015). A Statistical Framework for Analysing Big Data, June 2015, cat. no , ABS, Canberra. siu-ming.tam@abs.gov.au 14
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