Statistical theory

The theory of statistics provides a basis for the whole range of techniques, in both study design and data analysis, that are used within applications of statistics. The theory covers approaches to statistical-decision problems and to statistical inference, and the actions and deductions that satisfy the basic principles stated for these different approaches. Within a given approach, statistical theory gives ways of comparing statistical procedures; it can find a best possible procedure within a given context for given statistical problems, or can provide guidance on the choice between alternative procedures. Apart from philosophical considerations about how to make statistical inferences and decisions, much of statistical theory consists of mathematical statistics, and is closely linked to probability theory, to utility theory, and to optimization. Statistical theory provides an underlying rationale and provides a consistent basis for the choice of methodology used in applied statistics. Statistical models describe the sources of data and can have different types of formulation corresponding to these sources and to the problem being studied. Such problems can be of various kinds: Sampling from a finite population Measuring observational error and refining procedures Studying statistical relations Statistical models, once specified, can be tested to see whether they provide useful inferences for new data sets. Statistical theory provides a guide to comparing methods of data collection, where the problem is to generate informative data using optimization and randomization while measuring and controlling for observational error. Optimization of data collection reduces the cost of data while satisfying statistical goals, while randomization allows reliable inferences. Statistical theory provides a basis for good data collection and the structuring of investigations in the topics of: Design of experiments to estimate treatment effects, to test hypotheses, and to optimize responses.

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"