Minimax-regret sample design in anticipation of missing data, with application to panel data

  title={Minimax-regret sample design in anticipation of missing data, with application to panel data},
  author={Jeff Dominitz and Charles F. Manski},
  journal={Journal of Econometrics},
Abstract Missing data problems are ubiquitous in data collection. In surveys, these problems may arise from unit response, item nonresponse, and panel attrition. Building on the Dominitz and Manski (2017) study of choice between two or more sampling processes that differ in cost and quality, we study minimax-regret sample design in anticipation of missing data, where the collected data will be used for prediction under square loss of the values of functions of two variables. The analysis… Expand
2 Citations
Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald
This paper proposes statistical decision theory as a framework for evaluation of the performance of models in decision making, and considers the common practice of as-if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. Expand
Statistical inference for statistical decisions
This paper motivates inference-based SDFs as practical procedures for decision making that may accomplish some of what Wald envisioned and presents specific findings concerning treatment choice and point prediction with sample data. Expand


Pseudo Panels and Repeated Cross-Sections
In many countries there is a lack of genuine panel data where specific individuals or firms are followed over time. However, repeated cross-sectional surveys may be available, where a random sampleExpand
More Data or Better Data? A Statistical Decision Problem
When designing data collection, crucial questions arise regarding how much data to collect and how much effort to expend to enhance the quality of the collected data. To make choice of sample designExpand
Nonresponse Rates and Nonresponse Bias in Household Surveys
Many surveys of the U.S. household population are experiencing higher refusal rates. Nonresponse can, but need not, induce nonresponse bias in survey estimates. Recent empirical findings illustrateExpand
Identification and estimation of dynamic models with a time series of repeated cross-sections
Abstract Repeated cross-sectional data contain information on independent cross-sections of individual units at two or more points in time. Estimation of dynamic models with such data is madeExpand
Panel data from time series of cross-sections
Abstract In many countries, there are few or no panel data, but there exists a series of independent cross-sections. For example, in the United Kingdom, there are no panel data on consumers'Expand
Clinical trial design enabling {\epsilon}-optimal treatment rules
Medical research has evolved conventions for choosing sample size in randomized clinical trials that rest on the theory of hypothesis testing. Bayesians have argued that trials should be designed toExpand
Multivariate Bernoulli distribution
In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes. This distribution is discussed in the framework of the exponentialExpand
Total Survey Error: Past, Present, and Future
Total survey error is a conceptual framework describing statistical error properties of sample survey statistics. Early in the history of sample surveys, it arose as a tool to focus on implicationsExpand