{'fraction_non_alphanumeric': 0.020227560050568902, 'fraction_numerical': 0.0, 'mean_word_length': 5.947368421052632, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Innovative Imputation Techniques Designed for the Agricultural Resource Management Survey

The Agricultural Resource Management Survey (ARMS) is a high dimensional, complex economic survey which suffers from item non-response. Here, we introduce methods of varying complexity for imputation in this survey. The methods include stratified mean imputation, the approximate Bayesian bootstrap, and non-iterative and iterative sequential regression. The iterative sequential regression is a form of Markov chain Monte Carlo (MCMC) that is unique in that it allows for flexible selection of conditional distributions while utilizing joint modeling. Each of the regression procedures require data-driven transformations that allow for the implementation of a conditional multivariate normal model.