05663oam 22012254 450 991097378120332120250426110743.0978661284134797814623911101462391117978145278641414527864109781451870411145187041897812828413451282841343(CKB)3170000000055083(EBL)1607966(SSID)ssj0000944161(PQKBManifestationID)11503328(PQKBTitleCode)TC0000944161(PQKBWorkID)10983260(PQKB)10048888(OCoLC)761981611(IMF)WPIEE2008183(MiAaPQ)EBC1607966(IMF)WPIEA2008183WPIEA2008183(EXLCZ)99317000000005508320020129d2008 uf 0engur|n|---|||||txtccrKernel Density Estimation Based on Grouped Data : The Case of Poverty Assessment /Camelia Minoiu, Sanjay Reddy1st ed.Washington, D.C. :International Monetary Fund,2008.1 online resource (36 p.)IMF Working PapersIMF working paper ;WP/08/183Description based upon print version of record.9781451914948 1451914946 Includes bibliographical references.Contents; I. Motivation; II. The Data Structure and the Bias of the Estimator; III. The Bandwidth and Kernels Considered; IV. Monte Carlo Study; A. Theoretical Distributions; B. Summary Statistics, Density Estimates and Diagrams; C. Poverty Estimates; V. Country Studies; VI. Global Poverty; VII. Conclusions; References; Appendix; Appendix Figures; 1. Distributions used in Monte Carlo analysis; 2. Bias of KDE-based density (log-normal distribution); Appendix Tables; 1. Summary statistics from KDE-based sample; 3. Bias of estimated density (multimodal distribution)4. Bias of estimated density (Dagum distribution)2. Bias of poverty measures (Low and High Poverty Lines); 5. Bias in the poverty headcount ratio versus location of poverty line; 3. Bias of poverty measures (Triweight kernel, Poverty line: 0.25 x median); 4. Bias of poverty measures (Hybrid bandwidth, Poverty line: 0.5 x median); 5. Bias of poverty measures (Epanechnikov kernel, Silverman bandwidth); 6. Bias of poverty measures (Gaussian kernel, Poverty line: Capability); 6. Survey-based and grouped data KDE-based density estimates; 7. Global poverty rates (% poor)8. Global poverty counts (millions)We analyze the performance of kernel density methods applied to grouped data to estimate poverty (as applied in Sala-i-Martin, 2006, QJE). Using Monte Carlo simulations and household surveys, we find that the technique gives rise to biases in poverty estimates, the sign and magnitude of which vary with the bandwidth, the kernel, the number of datapoints, and across poverty lines. Depending on the chosen bandwidth, the $1/day poverty rate in 2000 varies by a factor of 1.8, while the $2/day headcount in 2000 varies by 287 million people. Our findings challenge the validity and robustness of poverty estimates derived through kernel density estimation on grouped data.IMF Working Papers; Working Paper ;No. 2008/183PovertyMeasurementIncome distributionEconometric modelsKernel functionsAggregate Factor Income DistributionimfDemographic Economics: GeneralimfDemographyimfEconometric modelsimfEconometrics & economic statisticsimfEconometricsimfEstimation techniquesimfEstimationimfIncome distributionimfIncomeimfMacroeconomicsimfPersonal incomeimfPersonal Income, Wealth, and Their DistributionsimfPopulation & demographyimfPopulation and demographicsimfPopulationimfPoverty & precarityimfPoverty and HomelessnessimfPovertyimfWelfare, Well-Being, and Poverty: GeneralimfNicaraguaimfPovertyMeasurement.Income distributionEconometric models.Kernel functions.Aggregate Factor Income DistributionDemographic Economics: GeneralDemographyEconometric modelsEconometrics & economic statisticsEconometricsEstimation techniquesEstimationIncome distributionIncomeMacroeconomicsPersonal incomePersonal Income, Wealth, and Their DistributionsPopulation & demographyPopulation and demographicsPopulationPoverty & precarityPoverty and HomelessnessPovertyWelfare, Well-Being, and Poverty: General339.46Minoiu Camelia874355Reddy Sanjay602369DcWaIMFBOOK9910973781203321Kernel Density Estimation Based on Grouped Data4372836UNINA03761nam 2200577 a 450 991095767320332120240410184400.00-7618-8632-X1-283-61427-80-7618-4933-59786613926722(CKB)3360000000435106(OCoLC)813285786(CaPaEBR)ebrary10606952(SSID)ssj0000720037(PQKBManifestationID)12350355(PQKBTitleCode)TC0000720037(PQKBWorkID)10661021(PQKB)11586977(Au-PeEL)EBL3031631(CaPaEBR)ebr10606952(CaONFJC)MIL392672(OCoLC)817814073(MiAaPQ)EBC3031631(EXLCZ)99336000000043510620090904d2010 uy 0engurcn|||||||||txtccrBasic statistics for social workers /Robert A. SchneiderRev. ed.Lanham, Md. University Press Of Americac20101 online resource (112 p.) Includes index.0-7618-2606-8 0-7618-4932-7 Cover -- Title Page -- Copyright Page -- Dedication Page -- Table of Contents -- Preface to the Revised Edition -- For the Instructor -- Chapter One: Introduction and Variables -- 1. Definition of statistics -- 2. Three reasons to learn some statistics -- 3. Quantitative vs. Qualitative Data -- 4. Variables and Attributes -- 5. Practice Problems -- Chapter Two: Levels of Measurement -- 1. Nominal Level -- 2. Ordinal Level Data -- 3. Interval Level Data -- 4. Ratio Level Data -- 5. Practice Problems -- Chapter Three: Data Representation -- 1. Frequency Tables -- 2. Crosstabs -- 3. Histograms -- 4. Percentiles -- 5. Practice Problems -- Chapter Four: Measures of Central Tendency -- 1. Mean -- 2. Median -- 3. Mode -- 4. Statistics and Parameters -- 5. Skewness -- 6. Practice Problems -- Chapter Five: Measures of Distribution -- 1. Range -- 2. Standard Deviation and Variance -- 3. Z-Scores -- 4. Areas Under the curve -- 5. Practice Problems -- Chapter Six: Inference -- 1. Sampling -- 2. Probability and P-Values -- 3. Type I Error -- 4. Type II Error -- 5. Confidence Intervals -- 6. Practice Problems -- Chapter Seven: Simple Correlations -- 1. Pearson Product-Moment Correlations -- 2. Spearman and Point Biserial -- 3. Shared Variance and Error -- 4. Causality -- 5. Practice Problems -- Chapter Eight: Bivariate Regression -- 1. Practice Problems -- Chapter Nine: Multiple Regression -- 1. Basics -- 2. Practice Problems -- Chapter Ten: t-Test and One-way Analysis of Variance -- 1. t-Test -- 2. Directional t-test -- 3. One-Way Analysis of Variance -- 4. Practice Problems -- Chapter Eleven: Chi Square -- 1. Calculating Expected Values in Chi Square -- 2. Fisher's Exact -- 3. Phi -- 4. Practice Problems -- Chapter Twelve: Single System Analysis -- 1. Practice Problems -- Tables -- About the Author -- Index.This revised edition was developed after teaching statistics to undergraduate and graduate social work students for over ten years. The statistical concepts that are necessary for students to know are covered, ranging from simple descriptive statistics such as crosstabs and tabular data up to a limited discussion of multiple regression.Social serviceStatistical methodsSocial serviceStatistical methods.519.5Schneider Robert A43564MiAaPQMiAaPQMiAaPQBOOK9910957673203321Basic statistics for social workers4454818UNINA