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[下载]硕士学位论文 农村居民食物消费结构变动及其对粮食需求影响的实证分析 [推广有奖]

311
SPSSCHEN 发表于 2006-5-2 12:21:00

Literature: The Multilevel Modeling

  • Bressoux, Pascal, Coustère, Paul & Leroy-Audouin, Christine (1997): Les modèles multiniveau dans l'analyse écologique: le case de la recherche en éducation, in: Revue française de sociologie, 38, 67-96.
    Some like it French: An introductory paper on multilevel analysis of educational achievement, with good data. No maths.

  • Bryk, A. S. & Raudenbush, S. W. (1992): Hierarchical Linear Models. Applications and Data Analysis Methods. Newbury Park, CA: Sage.
    Very good for those who wish to arrive at an advanced understanding.

  • DiPrete, Thomas A. & Forristal, Jerry D. (1994): Multilevel Models: Methods and Substance, in: Annual Review of Sociology, 20, 331-357.
    A good overview of the basic ideas and of applications, with most emphasis on random-coefficient models.

  • Ditton, Hartmut (1998): Mehrebenenanalyse. Grundlagen und Anwendungen des Hierarchisch Linearen Modells. Weinheim und München: Juventa.
    This book is specifically useful for those who want to analyze school data (and have to resort to books in German). However, readers of chapter 3 (dealing largely with centering) should consult chapter 5.2 in Kreft/de Leeuw 1998 and the paper by Kreft/de Leeuw/Aiken 1995.

  • Engel, Uwe (1998): Einführung in die Mehrebenenanalyse. Opladen: Westdeutscher Verlag (WV Studium 182).
    The wide coverage of this book has much to recommend it - for users who already have acquired an elementary understanding.

  • Goldstein, Harvey (1995): Multilevel Statistical Models. London: Arnold.
    A more advanced introduction by one of the " fathers" of multilevel modeling, now (2003) in its third ediation. An earlier version of the book can be downloaded here.

  • Hox, Joop (1995): Multilevel Analysis. Techniques and Applications. Mahwah, NJ: Erlbaum.
    This book is the expanded and updated version of an earlier book, Multilevel Analysis, Amsterdam: TT Publishers, 1995, which is downloadable for free (attention, this is a PDF file with a size of several MB).

  • Hox, Joop J. & Kreft, Ita G. G. (1994): Multilevel Analysis Methods, in: Sociological Methods & Research, 22, 283-299.
    This introduction to a special issue of SMR gives a brief overview of problems resulting from using traditional approaches for multilevel data, of the basic structure of the random coefficient model, of available software, and of current problems and possible future developments.

  • Kreft, Ita G. G. (1991): Using Hierarchically Linear Models to Analyse Multilevel Data. In: ZUMA-Nachrichten, 29, 44-56.
    A brief introduction that should be found in most German sociology libraries, but (naturally) cannot replace a textbook.

  • Kreft, Ita & de Leeuw, Jan (1998): Introducing Multilevel Modeling, London: Sage.
    This is certainly the most helpful textbook for those with a strong dislike of maths, formal derivations and the like. The reader is carefully guided through a number of examples. However, one should be aware that there are many advanced topics that are not dealt with in this book.

  • Longford, N. (1993): Random coefficient models. Oxford: Oxford University Press.
    In this book, statistical reasoning is paramount, but it is also applied to several datasets with helpful discussions of the results.

  • Ohr, Dieter (1999): Modellierung von Kontexteffekten: Voraussetzungen, Verfahren und eine empirische Anwendung am Beispiel des politischen Informationsverhaltens, in: ZA- Information 44, 39-63.
    A fine brief introduction, but I feel that it is somewhat infortunate that MM is introduced via an example where the gains from using a MM approach are almost nil (statistically speaking, i.e. the MM analysis barely differs from OLS regression results).

  • Snijders, Tom & Bosker, Roel (1999): Multilevel Analysis. An introduction to basic and advanced multilevel modeling. London, Thousand Oaks: SAGE.
    This is a very thorough introduction that requires quite some effort on part of the student - but it pays. Especially chapters 8 and 9 should be studied carefully, as I have found no comparable discussion of heteroscedascity and the basic assumptions (and how to check them) of multilevel modeling.

[此贴子已经被作者于2006-5-2 12:22:32编辑过]

312
Trevor 发表于 2006-5-3 00:14:00

Multivariate Analysis

  • Introduction
  • Matrix Algebra
  • What is an SSCP Matrix?
  • The Seven Basic Matrices of Multivariate Analysis
  • Computing the Deviation SSCP
  • Matrix Magic
  • The Multivariate Normal Distribution
  • Regression Analysis
  • Probit Analysis
  • Hypothesis Testing: 1 & 2 Groups
  • Hypothesis Testing: k-Groups
  • Profile Analysis
  • Hypothesis Testing: Equality of Covariance Matrices
  • More on Matrices
  • Discriminant Analysis
  • Classification of Observations
  • Canonical Correlation Analysis
  • The Big Picture
  • Multivariate Data: The Long and the Wide of It
  • Factorial Multivariate Analysis of Variance
  • Variations in the Key of F
  • General Linear Model
  • Principal Components and Factor Analysis Models
  • Linear Structural Models
  • Cluster Analysis
  • Multidimensional Scaling
  • Correspondence Analysis
  • Latent Class and Mixture Models
  • 313
    Trevor 发表于 2006-5-3 00:16:00

    http://www.core.org.cn/OcwWeb/Civil-and-Environmental-Engineering/1-017Computing-and-Data-Analysis-for-Environmental-ApplicationsFall2003/LectureNotes/index.htm

    Course Introduction (PDF)
    2 Descriptive Statistics (PDF)
    3 Probablility (PDF) virtual.m (M)
    4 Joint Probability, Independence, Repeated Trials (PDF)
    5 Combinatorial Methods for Deriving Probabilities (PDF) combinatorial_example.pdf (PDF)
    balls.m (M)
    6 Conditional Probability and Baye's Theorem (PDF)
    7 Random Variables and Probability Distributions (PDF)
    8 Expectation, Functions of a Random Variable (PDF)
    9 Risk
    10 Some Common Probability Distributions (PDF) cdffit.m (M)
    11 Multivariate Probability (PDF)
    12 Functions of Many Random Variables
    13 Populations and Samples (PDF)
    14 Estimation (PDF)
    15 Confidence Intervals (PDF)
    16 Testing Hypotheses about a Single Population (PDF)
    17 Testing Hypotheses about Two Populations (PDF)
    18 Small Sample Statistics (PDF)
    19 Analysis of Variance (PDF)
    20 Analysis of Variance (contd.) (PDF)
    21 Multifactor Analysis of Variance (PDF)
    22 Linear Regression (PDF)
    23 Analyzing Regression Results (PDF)

    [此贴子已经被作者于2006-5-3 0:17:51编辑过]

    314
    Multivariate 发表于 2006-5-3 01:48:00

    [下载][推荐]Alan Duncan.Lecture Notes.CROSS-SECTION AND PANEL DATA ECONOMETRICS

    Lecture 1 Binary Choice Models
    The Linear Probability Model; binomial probit; binomial logit; assumptions; Maximum Likelihood estimation methods; interpretation of coefficients; constructing probabilities; restrictions and limitations; marginal effects; measuring goodness-of fit; testing parameter restrictions.
    Downloads: [lecture notes] [overheads] [exercise] [binary choice estimates]

    Lecture 2 Multiple Discrete Choice Models
    Ordered probit/logit; sequential probit/logit; methods of estimation; multinomial logit (MNL); the Independence of Irrelevant Alternatives (IIA) assumption; bivariate and Multinomial Probit models; measuring goodness-of fit; testing assumptions.
    Downloads: [lecture notes] [overheads]

    Lecture 3 Limited Dependent Variable Models 1I
    truncated and censored samples; sample selection bias; the truncated regression model; marginal effects; the Tobit model; interpretation of Tobit model coefficients; testing for normality; limitations of the Tobit model.

    Downloads: [lecture notes] [overheads]

    Lecture 4 Limited Dependent Variable Models 2
    bivariate generalisations of the Tobit model; the Selectivity(Heckit) model; two-step and full-information estimation methods; interpretation of model coefficients; diagnostic testing; the Double Hurdle (DH) model; the DH model with dependence; switching regressions; diagnostic testing.
    Downloads: [lecture notes] [overheads]

    Lecture 5 Duration Models and Survival Functions
    the concept of duration and survival; parametric hazard and survival functions; duration dependence; methods of estimation; the proportional hazard models, introducing heterogeneity; time-invariant and time-varying covariates.
    Downloads: [lecture notes] [overheads]

    Lecture 6 Panel Data Models
    general definitions; fixed effects and random effects panel data models; methods of estimation; random coefficients; discrete choice panel data models; diagnostic testing; dynamic and nonlinear panel data models.
    Downloads: [lecture notes] [overheads]

    Lecture 7 Nonparametric and Semiparametric Estimation Methods
    general definitions; kernel density estimation; Nadaraya-Watson nonparametric regression function; bandwidth selection; average derivative estimation; bootstrap methods and confidence bands; semiparametric estimation methods; partially linear models
    Downloads: [survey paper]

    [此贴子已经被作者于2006-5-3 1:51:04编辑过]

    315
    hanszhu 发表于 2006-5-3 09:33:00

    316
    hanszhu 发表于 2006-5-3 09:34:00

    317
    leaftable 发表于 2006-5-3 12:11:00

    为了论坛的兴盛繁荣,建议降价促销

    o︻$▅▆▇◤ 呵,我就不信这么大把钥匙还打不开你门

    318
    hanszhu 发表于 2006-5-3 21:33:00

    [下载]David Henry.Bridging the Gap.Linking Economics and Econometrics.pdf

    51140.pdf (157.29 KB)

    319
    Nicolle 学生认证  发表于 2006-5-5 00:46:00

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    320
    hanszhu 发表于 2006-5-5 03:52:00

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