Media mix modeling is a statistical analysis on historical data to measure the return on investment (ROI) on advertising and other marketing activities. Current practice usually utilizes data aggregated at a national level, which often suffers from small sample size and insufficient variation in the media spend. When sub-national data is available, we propose a geo-level Bayesian hierarchical m...

There are currently two major agencies, the Bureau of Labor Statistics (BLS) and the Bureau of Economic Analysis (BEA), that produce estimates of the cost of shelter for renters and for owners on a regular basis. In addition, the Census Bureau is conducting a nation-wide survey, the American Community Survey (ACS) of rents and owner costs on a rolling five-year basis. This paper explores the fe...

A dividend imputation tax system provides shareholders with a credit (for corporate tax paid) that can be used to offset personal tax on dividend income. This paper shows how to infer the value of imputation tax credits from the prices of derivative securities that are unique to Australian retail markets. We also test whether a tax law amendment that was designed to prevent the trading of imput...

We present a model-based approach for imputing missing person age for the 2000 Census short form. We use a series of easy to implement multiple regression models, first proposed at the 1997 ASA Joint Meetings, to predict the missing age of a person. This paper extends our work by comparing our results with that of the hot-deck method used for the 1990 Census. By using a comprehensive set of inf...

Iterative imputation, in which variables are imputed one at a time each given a model predicting from all the others, is a popular technique that can be convenient and flexible, as it replaces a potentially difficult multivariate modeling problem with relatively simple univariate regressions. In this paper, we begin to characterize the stationary distributions of iterative imputations and their...

We consider three sorts of diagnostics for random imputations: (a) displays of the completed data, intended to reveal unusual patterns that might suggest problems with the imputations, (b) comparisons of the distributions of observed and imputed data values, and (c) checks of the fit of observed data to the model used to create the imputations. We formulate these methods in terms of sequential ...

In this paper we describe some applications of the Random Recursive Partitioning (RRP) method. This method generates a proximity matrix which can be used in non parametric hot-deck missing data imputation, classification, prediction, average treatment effect estimation and, more generally, in matching problems. RRP is a Monte Carlo procedure that randomly generates non-empty recursive partition...