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1. |
Record Nr. |
UNINA9910830449303321 |
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Autore |
Lui Kung-Jong |
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Titolo |
Statistical estimation of epidemiological risk [[electronic resource] /] / Kung-Jong Lui |
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Pubbl/distr/stampa |
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Chichester ; ; Hoboken, NJ, : Wiley, c2004 |
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ISBN |
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1-280-26954-5 |
9786610269549 |
0-470-09407-9 |
0-470-09408-7 |
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Descrizione fisica |
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1 online resource (213 p.) |
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Collana |
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Disciplina |
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Soggetti |
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Epidemiology - Statistical methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Statistical Estimation of Epidemiological Risk; Contents; About the author; Preface; 1 Population Proportion or Prevalence; 1.1 Binomial sampling; 1.2 Cluster sampling; 1.3 Inverse sampling; Exercises; References; 2 Risk Difference; 2.1 Independent binomial sampling; 2.2 A series of independent binomial sampling procedures; 2.2.1 Summary interval estimators; 2.2.2 Test for the homogeneity of risk difference; 2.3 Independent cluster sampling; 2.4 Paired-sample data; 2.5 Independent negative binomial sampling (inverse sampling); 2.6 Independent poisson sampling; 2.7 Stratified poisson sampling |
ExercisesReferences; 3 Relative Difference; 3.1 Independent binomial sampling; 3.2 A series of independent binomial sampling procedures; 3.2.1 Asymptotic interval estimators; 3.2.2 Test for the homogeneity of relative difference; 3.3 Independent cluster sampling; 3.4 Paired-sample data; 3.5 Independent inverse sampling; Exercises; References; 4 Relative Risk; 4.1 Independent binomial sampling; 4.2 A series of independent binomial sampling procedures; 4.2.1 Asymptotic interval estimators; 4.2.2 Test for the homogeneity of risk ratio; 4.3 Independent cluster sampling; 4.4 Paired-sample data |
4.5 Independent inverse sampling4.5.1 Uniformly minimum variance |
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unbiased estimator of relative risk; 4.5.2 Interval estimators of relative risk; 4.6 Independent poisson sampling; 4.7 Stratified poisson sampling; Exercises; References; 5 Odds Ratio; 5.1 Independent binomial sampling; 5.1.1 Asymptotic interval estimators; 5.1.2 Exact confidence interval; 5.2 A series of independent binomial sampling procedures; 5.2.1 Asymptotic interval estimators; 5.2.2 Exact confidence interval; 5.2.3 Test for homogeneity of the odds ratio; 5.3 Independent cluster sampling; 5.4 One-to-one matched sampling |
5.5 Logistic modeling5.5.1 Estimation under multinomial or independent binomial sampling; 5.5.2 Estimation in the case of paired-sample data; 5.6 Independent inverse sampling; 5.7 Negative multinomial sampling for paired-sample data; Exercises; References; 6 Generalized Odds Ratio; 6.1 Independent multinomial sampling; 6.2 Data with repeated measurements (or under cluster sampling); 6.3 Paired-sample data; 6.4 Mixed negative multinomial and multinomial sampling; Exercises; References; 7 Attributable Risk; 7.1 Study designs with no confounders; 7.1.1 Cross-sectional sampling |
7.1.2 Case-control studies7.2 Study designs with confounders; 7.2.1 Cross-sectional sampling; 7.2.2 Case-control studies; 7.3 Case-control studies with matched pairs; 7.4 Multiple levels of exposure in case-control studies; 7.5 Logistic modeling in case-control studies; 7.5.1 Logistic model containing only the exposure variables of interest; 7.5.2 Logistic regression model containing both exposure and confounding variables; 7.6 Case-control studies under inverse sampling; Exercises; References; 8 Number Needed to Treat; 8.1 Independent binomial sampling |
8.2 A series of independent binomial sampling procedures |
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Sommario/riassunto |
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Statistical Estimation of Epidemiological Risk provides coverage of the most important epidemiological indices, and includes recent developments in the field. A useful reference source for biostatisticians and epidemiologists working in disease prevention, as the chapters are self-contained and feature numerous real examples. It has been written at a level suitable for public health professionals with a limited knowledge of statistics. Other key features include:Provides comprehensive coverage of the key epidemiological indices.Includes coverage of various sampling methods |
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2. |
Record Nr. |
UNINA9910484686603321 |
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Titolo |
Combinatorial Pattern Matching : 21st Annual Symposium, CPM 2010, New York, NY, USA, June 21-23, 2010, Proceedings, / / edited by Amihood Amir, Laxmi Parida |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010 |
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ISBN |
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1-280-38700-9 |
9786613564924 |
3-642-13509-9 |
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Edizione |
[1st ed. 2010.] |
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Descrizione fisica |
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1 online resource (XIII, 362 p. 84 illus.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 6129 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Pattern recognition systems |
Life sciences |
Computer programming |
Algorithms |
Artificial intelligence - Data processing |
Data mining |
Automated Pattern Recognition |
Life Sciences |
Programming Techniques |
Data Science |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Algorithms for Forest Pattern Matching -- Affine Image Matching Is Uniform -Complete -- Old and New in Stringology -- Small-Space 2D Compressed Dictionary Matching -- Bidirectional Search in a String with Wavelet Trees -- A Minimal Periods Algorithm with Applications -- The Property Suffix Tree with Dynamic Properties -- Approximate All-Pairs Suffix/Prefix Overlaps -- Succinct Dictionary Matching with No Slowdown -- Pseudo-realtime Pattern Matching: Closing the Gap -- |
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Breakpoint Distance and PQ-Trees -- On the Parameterized Complexity of Some Optimization Problems Related to Multiple-Interval Graphs -- Succinct Representations of Separable Graphs -- Implicit Hitting Set Problems and Multi-genome Alignment -- Bounds on the Minimum Mosaic of Population Sequences under Recombination -- The Highest Expected Reward Decoding for HMMs with Application to Recombination Detection -- Phylogeny- and Parsimony-Based Haplotype Inference with Constraints -- Faster Computation of the Robinson-Foulds Distance between Phylogenetic Networks -- Mod/Resc Parsimony Inference -- Extended Islands of Tractability for Parsimony Haplotyping -- Sampled Longest Common Prefix Array -- Verifying a Parameterized Border Array in O(n 1.5) Time -- Cover Array String Reconstruction -- Compression, Indexing, and Retrieval for Massive String Data -- Building the Minimal Automaton of A * X in Linear Time, When X Is of Bounded Cardinality -- A Compact Representation of Nondeterministic (Suffix) Automata for the Bit-Parallel Approach -- Algorithms for Three Versions of the Shortest Common Superstring Problem -- Finding Optimal Alignment and Consensus of Circular Strings -- Optimizing Restriction Site Placement for Synthetic Genomes -- Extension and Faster Implementation of the GRP Transform for Lossless Compression -- Parallel andDistributed Compressed Indexes. |
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Sommario/riassunto |
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The papers contained in this volume were presented at the 21st Annual S- posium on Combinatorial Pattern Matching (CPM 2010) held at NYU-Poly, Brooklyn, New York during June 21-23, 2010. Allthe paperspresentedatthe conferenceareoriginalresearchcontributions. We received 53 submissions from 21 countries. Each paper was reviewed by at least three reviewers. The committee decided to accept 28 papers. The program also includes three invited talks by Zvi Galil from Tel Aviv University, Israel, Richard M. Karp from University of California at Berkeley, USA, and Je'rey S. Vitter from Texas A&M University, USA. The objective of the annual CPM meetings is to provide an international forum for research in combinatorial pattern matching and related applications. It addresses issues of searching and matching strings and more complicated p- terns such as trees, regular expressions, graphs, point sets, and arrays. The goal is to derive non-trivialcombinatorialproperties of suchstructures and to exploit these properties in order to either achieve superior performance for the cor- sponding computational problems or pinpoint conditions under which searches cannot be performed e'ciently. The meeting also deals with problems in c- putational biology, data compression and data mining, coding, information - trieval, natural language processing and pattern recognition. TheAnnual SymposiumonCombinatorialPatternMatchingstartedin 1990, andhassincetakenplaceeveryyear.PreviousCPM meetingswereheld inParis, London, Tucson, Padova, Asilomar, Helsinki, Laguna Beach, Aarhus, Pisc- away, Warwick, Montreal, Jerusalem, Fukuoka, Morelia, Istanbul, Jeju Island, Barcelona, London, Ontario, Pisa, and Lille. |
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