It takes the concept ot the likelihood as providing the best methods for unifying the demands of statistical modelling and the theory of inference. Every likelihood concept is illustrated by realistic examples, which are not compromised by computational problems. Examples range from a simile comparison of two accident rates, to complex studies that require generalised linear or semiparametric modelling. The emphasis is that the likelihood is not simply a device to produce an estimate, but an important tool for modelling.
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Software Publications For more info on the statistical modelling book , Data and Software , please see the tabs above. For ongoing works on sequencing data analysis go to our biostatwiki page Books Pawitan Y: In All Likelihood: Statistical modeling and inference using likelihood. Oxford University Press. Cell-level somatic mutation detection from single-cell RNA sequencing.
On the relationship between the heritability and the attributable fraction. Hum Genet. Epub Apr 2. Sci Rep. Disease trajectories and mortality among women diagnosed with breast cancer. Breast Cancer Res. Antibiotics use and risk of amyotrophic lateral sclerosis in Sweden. Eur J Neurol. Epub Jun 7. Isoform-level gene expression patterns in single-cell RNA-sequencing data, Bioinformatics, Jul 15;34 14 Generalized survival models for correlated time-to-event data.
Stat Med. Regression standardization and attributable fraction estimation with between-within frailty models for clustered survival data. Stat Methods Med Res. Mol Ecol Resour. Pawitan Y and Lee Y. Wallet game: probability, likelihood and extended likelihood.
The American Statistician , A clinical model for identifying the short-term risk of breast cancer. Patterns of acute inflammatory symptoms prior to cancer diagnosis. The ABC model of prostate cancer: A conceptual framework for the design and interpretation of prognostic studies. Beta-Poisson model for single-cell RNA-seq data analyses. Model-based estimation of the attributable fraction for cross-sectional, case-control and cohort studies using the R package AF.
Eur J Epidemiol. Genet Epidemiol. Alzheimers Dement. Twin Res Hum Genet. Nonparametric estimation of the rediscovery rate. Doubly robust methods for handling confounding by cluster. Likelihood ratio and score burden tests for detecting disease-associated rare variants. Stat Appl Genet Mol Biol. Biomed Res Int. Epub Aug 3. Sparse estimation of gene-gene interactions in prediction models.
Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival. PLoS One. Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity. Hum Mol Genet. Epub Sep A simplified interventional mapping system SIMS for the selection of combinations of targeted treatments in non-small cell lung cancer. Molecular differences in transition zone and peripheral zone prostate tumors.
Epub Apr Distinct effects of anti-inflammatory and anti-thrombotic drugs on cancer characteristics at diagnosis. Eur J Cancer. Cancer Res. Rediscovery rate estimation for assessing the validation of significant findings in high-throughput studies.
Brief Bioinform. Bounds on sufficient-cause interaction. Bounds on causal interactions for binary outcomes. Most genetic risk for autism resides with common variation. Nat Genet. Epub Jul Aspirin intake and breast cancer survival — a nation-wide study using prospectively recorded data in Sweden.
BMC Cancer. Affinity proteomics reveals elevated muscle proteins in plasma of children with cerebral malaria. PLoS Pathog.
Prostate Cancer Prostatic Dis. Joint estimation of isoform expression and isoform-specific read distribution using multisample RNA-Seq data. Integrated molecular portrait of non-small cell lung cancers.
BMC Med Genomics. Sparse partial least-squares regression for high-throughput survival data analysis. Partial least squares and logistic regression random-effects estimates for gene selection in supervised classification of gene expression data.
J Biomed Inform. Low-dose aspirin use and cancer characteristics: a population-based cohort study. Br J Cancer. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Quantitative proteomics profiling of primary lung adenocarcinoma tumors reveals functional perturbations in tumor metabolism. J Proteome Res. Between-within models for survival analysis. Analysis of matched cohort studies and twin studies, with binary exposures and binary outcomes.
Statistical Science 27 3 , — Bioinformatics, Nov 1;28 21 Epub Aug Network enrichment analysis: extension of gene-set enrichment analysis to gene networks. BMC Bioinformatics, Sep 11; Hum Mutat. Verifying elimination programs with a special emphasis on cysticercosis endpoints and postelimination surveillance. J Parasitol Res. Epub Nov Is the association between general cognitive ability and violent crime caused by family-level confounders?
Validation of a radiosensitivity molecular signature in breast cancer. Clin Cancer Res. Advancing paternal age and offspring violent offending: a sibling-comparison study. Dev Psychopathol. A new paradigm emerges from the study of de novo mutations in the context of neurodevelopmental disease. Mol Psychiatry. Gene discovery in familial cancer syndromes by exome sequencing: prospects for the elucidation of familial colorectal cancer type X. Mod Pathol. Exome versus transcriptome sequencing in identifying coding region variants.
Expert Rev Mol Diagn. Technological advances in DNA sequence enrichment and sequencing for germline genetic diagnosis. Re-expression of microRNA reverses both tamoxifen resistance and accompanying EMT-like properties in breast cancer.
Human genetics and genomics a decade after the release of the draft sequence of the human genome. Hum Genomics. Regions of homozygosity in three Southeast Asian populations.
In All Likelihood: Statistical Modelling and Inference Using Likelihood
The book emphasizes that the likelihood is not simply a device to produce an estimate, but more importantly it is a tool for modeling. The book generally takes an informal approach, where most important results are established using heuristic arguments and motivated with realistic examples. With currently available computing power, examples are not contrived to allow a closed analytical solution, and the book concentrates on the statistical aspects of the data modelling. In addition to classical likelihood theory, the book covers many modern topics such as generalized linear models, generalized linear mixed models, nonparametric smoothing, robustness, EM algorithm and empirical likelihood. It takes the concept ot the likelihood as providing the best methods for unifying the demands of statistical modelling and the theory of inference.
In All Likelihood
In all likelihood by yudi pawitan