书目详细信息
： Statistics for data science and policy analysis /
书目信息 机读格式(MARC)
| ISBN： | 9789811517341 |
|---|---|
| ISBN： | 9789811517358 |
| ISBN： | 9811517355 |
| 其他标准号： | 10.1007/978-981-15-1735-8 |
| 编目源： | EBLCP EBLCP GW5XE YDX UPM OCLCQ EBLCP OCLCQ OCLCF UKAHL Uk |
| 会议名称： | Applied Statistics and Policy Analysis Conference |
| 题名： | Statistics for data science and policy analysis / Azizur Rahman, editor. |
| 索书号： | TB9-532/R147E |
| 载体形态： | xv, 386 pages : illustrations ; 24 cm |
| 一般附注： | "This book constitutes the refereed proceedings of the Applied Statistics and Policy Analysis Conference 2019 (ASPAC2019), held on 5-6 September 2019, in Wagga Wagga, NSW, Australia"--Page vii. |
| 一般附注： | Part II Agricultural Statistics and Policy Analysis |
| 书目附注： | Includes bibliographical references. |
| 格式化内容附注： | 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 |
| 格式化内容附注： | 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 |
| 格式化内容附注： | 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 |
| 格式化内容附注： | 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 |
| 格式化内容附注： | 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 |