Statistics for data science and policy analysis. (©2020)

• Record Type: Book
• Further Information: Note: Azizur Rahman, editor.
• Other Names: (Associate Professor in Mathematics and Statistics), Rahman, Azizur; Applied Statistics and Policy Analysis Conference
• Contents: Intro -- Preface -- Organisation -- Acknowledgements -- Contents -- Part I Applied Statistics and Bayesian Modeling -- 1 Applied Bayesian Modeling for Assessment of Interpretation Uncertainty in Spatial Domains -- 1.1 Introduction -- 1.2 Materials and Methods -- 1.2.1 Samples -- 1.2.2 Spatial Domains -- 1.2.3 Handheld X-Ray Fluorescence Measurement -- 1.2.4 Statistical Analysis Methods -- 1.2.4.1 Variable Selection -- 1.2.4.2 Bayesian Methodology -- 1.3 Results -- 1.4 Discussion -- References -- 2 Computing Robust Statistics via an EM Algorithm 2.1 Overview of Estimating Statistical Model Parameters -- 2.2 Model -- 2.3 Robust Estimation Based on Our Algorithm -- 2.4 Standard Error of -- 2.5 Choice of g -- 2.6 Simulation Studies -- 2.7 Applications -- 2.7.1 Speed of Light -- 2.7.2 Chem Data -- 2.7.3 Abbey Data -- 2.8 Conclusion -- Appendix A: Proof of Equation (2.9) -- References -- 3 Determining Risk Factors of Antenatal Care Attendance and its Frequency in Bangladesh: An Application of Count Regression Analysis -- 3.1 Introduction -- 3.2 Methods -- 3.2.1 Data Description -- 3.2.2 Statistical Models -- 3.2.3 Model Selection 3.3 Results and Discussion -- 3.4 Conclusions -- References -- 4 On Propensity Score Methodology -- 4.1 Propensity Score Methods -- Estimating Causal Effects -- 4.1.1 A Framework to Model Propensity Score Methods -- 4.2 Methods to Estimate the Effect of Treatment Using Propensity Scores -- 4.2.1 Matching on the Propensity Scores (Matching) -- 4.2.1.1 Matching "with" or "without" Replacement -- 4.2.2 Inverse Probability Weighing -- 4.2.3 Subclassification -- 4.2.4 Covariance Adjustment -- 4.2.5 Diagnostics of Matching, Subclassification and Covariate Adjustment Methods -- 4.2.5.1 Balance 4.2.5.2 Assessing Balance -- 4.2.5.3 Estimating Treatment Results -- 4.2.5.4 Alternate Methods to Estimate the Treatment Effect -- 4.2.6 Other Issues Identified Associated with Propensity Score Methodologies -- 4.2.6.1 Misspecification of the Dataset -- 4.2.6.2 Misspecification of the Matching, Subclassification and Covariate Adjustment Methods -- 4.3 Probability of Treatment Using Propensity Score Methods -- 4.3.1 Probability of Treatment Using Regression Methods -- 4.3.1.1 Logistic Regression -- 4.3.1.2 Probit Regression -- 4.3.2 Probability of Treatment Using Decision Trees 4.3.2.1 Generalized Boosted Model -- 4.3.3 Estimated Probability of Treatment Results -- 4.4 Propensity Score Methods Applied to Regional Centres -- 4.5 Concluding Remarks -- References -- 5 Asking Good Questions to Understand Voluntary Enrolments in Mathematics -- 5.1 Introduction -- 5.2 Motivation Model -- 5.3 Model Components -- 5.3.1 Self-Concept and Self-Efficacy in Mathematics -- 5.3.2 Subjective Task Values -- 5.3.3 Maths Anxiety -- 5.3.4 From Constructs to Items -- 5.4 Pilot Study and Validation -- 5.4.1 Participants -- 5.4.2 Procedure -- 5.4.3 Results -- 5.5 Remarks -- References
• Publisher Details: Singapore : Springer
• Publication Date: 2020
• Copyright Date: 2020
• Extent: 1 online resource (380 pages)
• Subjects: 519.5; Statistics -- Congresses; Big data -- Congresses; Big data; Statistics; Electronic books; Electronic books; Conference papers and proceedings
• Languages: English
• ISBNs: 9789811517358; 9811517355
• Related ISBNs: 9811517347; 9789811517341
• Notes: Note: Includes bibliographical references.; Note: Print version record.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.