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Time Series Analysis: Forecasting and Control. Englewood Cliffs; N. General anesthesia and altered states of arousal: A systems neuroscience analysis. Annual Review of Neuroscience.

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Personal omics profiling reveals dynamic molecular and medical phenotypes. Proceedings of the National Academy of Sciences. Tracking brain states under general anesthesia by using global coherence analysis. Age-dependent electroencephalogram EEG patterns during sevoflurane general anesthesia in infants. Dattner I, Klaassen CA.

Optimal rate of direct estimators in systems of ordinary differential equations linear in functions of the parameters. Electronic Journal of Statistics. Imagenet: A large-scale hierarchical image database.

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Privacy aware learning. Journal of the ACM. Two-stage sampling, prediction and adaptive regression via correlation screening sparcs. Optimal inference after model selection. Understanding the Issues. Home testing and counselling with linkage to care. The Lancet HIV. Haneuse S, Daniels M. A general framework for considering selection bias in EHR-based studies: What data are observed and why?

Convex modeling of interactions with strong heredity.

Scientific Inference: Learning from Data - Simon Vaughan - Google книги

Journal of Computational and Graphical Statistics. Hawkes AG. Spectra of some self-exciting and mutually exciting point processes. Henderson J, Michailidis G. Network reconstruction using nonparametric additive ODE models. Hero AO, Rajaratnam B. Large-scale correlation screening. Journal of the American Statistical Association. Hub discovery in partial correlation graphs. Foundational principles for large-scale inference: Illustrations through correlation mining.

Inference vs Prediction

Proceedings of the IEEE. Social collaborative retrieval. Huang Y. Integrative statistical learning with applications to predicting features of diseases and health [Ph. University of Michigan; Ann Arbor, Mich. Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza A infection. PLoS Genetics. The impact of model selection on inference in linear regression. The American Statistician.

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Hurvich CM, Zeger S. New York: New York University; Misunderstandings between experimentalists and observationalists about causal inference. Gene set enrichment analysis made simple. Statistical Methods in Medical Research. Selective ignorability assumptions in causal inference. The International Journal of Biostatistics. Statistical issues in the analysis of neuronal data.

Journal of Neurophysiology. When is hub gene selection better than standard meta-analysis? Lake Tahoe, Nev. On model selection consistency of M-estimators with geometrically decomposable penalties; pp. Exact post-selection inference, with application to the lasso. An individualized predictor of health and disease using paired reference and target samples. Joint and individual variation explained JIVE for integrated analysis of multiple data types.

Annals of Applied Statistics. A significance test for the lasso.

Examples for prediction and inference

High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. Advances in Neural Information Processing Systems. Cambridge, Mass.

Scottsdale, Ariz. Distributed learning of Gaussian graphical models via marginal likelihoods. Miettinen OS. The need for randomization in the study of intended effects. Statistics in Medicine. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes.

What you'll learn

Nature Genetics. Hypothesis testing for high-dimensional sparse binary regression. Washington, D. Frontiers in Massive Data Analysis. Pearl J. Causality: Models, Reasoning, and Inference. Cambridge, U. Spatio-temporal correlations and visual signalling in a complete neuronal population. Poole D, Raftery AE. Inference for deterministic simulation models: The Bayesian melding approach. Electroencephalogram signatures of loss and recovery of consciousness from propofol. Key concepts are developed through a combination of graphical explanations, worked examples, example computer code and case studies using real data.

Students will develop an understanding of the ideas behind statistical methods and gain experience in applying them in practice. Further resources are available at www. Cambridge University Press. Cambridge University Press dates from and is part of the University of Cambridge. We further the University's mission by disseminating knowledge in the pursuit of education, learning and research at the highest international levels of excellence. Skip Navigation and go to main content Bestsellers Books. Print this page. Includes delivery to Finland 2 copies available online - Usually dispatched within 24 hours.

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