Random k-nearest-neighbour (RKNN) imputation is an established algorithm for filling in missing values in data sets. Assume that data are missing in a random way, so that missingness is independent of unobserved values (MAR), and assume there is a minimum positive probability of a response vector being complete. Then RKNN, with k equal to the square root of the sample size, asymptotically produ...

Data Assimilation is commonly used in environmental sciences to improve forecasts, obtained by meteorological, oceanographic or air quality simulation models, with observation data. It aims to solve an evolution equation, describing the dynamics, and an observation equation, measuring the mis t between the state vector and the observations, to get a better knowledge of the actual system's state...

Background Routinely-collected data could be used to make randomised controlled trials (RCTs) more efficient, either for collection of outcome data or to enhance recruitment. The use of routine data in RCTs has been limited by concerns surrounding data quality, particularly missingness. To exploit these information-rich data sources, it is necessary to identify approaches capable of overcoming ...

Engineering optimization relies routinely on deterministic computer based design evaluations, typically comprising geometry creation, mesh generation and numerical simulation. Simple optimization routines tend to stall and require user intervention if a failure occurs at any of these stages. This motivated us to develop an optimization strategy based on surrogate modelling, which penalizes the ...

There are several methods to make inferences about the parameters of the sampling distribution when we encounter the missing values and the censored data. In this paper, through the order statistics and the projection theorem, a novel algorithm is proposed to impute the missing values in the multivariate case. Then, the performance of this method is investigated through the simulation studies. ...