Least squares algorithms with nearest neighbour techniques for imputing missing data values.

vii

[1]  R. E. Strauss,et al.  EVALUATION OF THE PRINCIPAL-COMPONENT AND EXPECTATION-MAXIMIZATION METHODS FOR ESTIMATING MISSING DATA IN MORPHOMETRIC STUDIES , 2003 .

[2]  J. Ross Quinlan,et al.  Unknown Attribute Values in Induction , 1989, ML.

[3]  J L Schafer,et al.  Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective. , 1998, Multivariate behavioral research.

[4]  “ Multiple Imputation in Practice : Comparison of Software Packages for Regression Models With Missing Variables , ” , 2002 .

[5]  D. Rubin.  Multiple Imputation After 18+ Years , 1996 .

[6]  Léon Bottou,et al.  Local Learning Algorithms , 1992, Neural Computation.

[7]  H. Timm,et al.  Di erent Approaches for Fuzzy Cluster Analysis with Missing Values , 1999 .

[8]  Amos Storkey,et al.  Advances in Neural Information Processing Systems 20 , 2007 .

[9]  Mary Ann. Hill,et al.  Spss Missing Value Analysis 7.5 , 1997 .

[10]  D. Rubin,et al.  The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence , 1994 .

[11]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[12]  J. Rao,et al.  Variance estimation under two-phase sampling with application to imputation for missing data , 1995 .

[13]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[14]  H. Kiers.  Weighted least squares fitting using ordinary least squares algorithms , 1997 .

[15]  Warren Sarle.  Prediction with Missing Inputs , 1998 .

[16]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[17]  Robert P. Goldman,et al.  Imputation of Missing Data Using Machine Learning Techniques , 1996, KDD.

[18]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[19]  B. Mirkin.  A sequential fitting procedure for linear data analysis models , 1990 .

[20]  B. Everitt,et al.  Finite Mixture Distributions , 1981 .

[21]  A. Arbor,et al.  Case-Based Learning Algorithms , 1991 .

[22]  J. G. Bethlehem,et al.  Data editing perspectives , 1997 .

[23]  Ingunn Myrtveit,et al.  Analyzing Data Sets with Missing Data: An Empirical Evaluation of Imputation Methods and Likelihood-Based Methods , 2001, IEEE Trans. Software Eng..

[24]  S. Nordbotten.  Neural network imputation applied to the Norwegian 1990 population census data , 1996 .

[25]  T. Ahonen,et al.  Treating Missing Data in a Clinical Neuropsychological Dataset–Data Imputation , 2001, The Clinical neuropsychologist.

[26]  Boris Mirkin,et al.  Mathematical Classification and Clustering , 1996 .

[27]  Per A. Hassel,et al.  Nonlinear partial least squares , 2003 .

[28]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[29]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[30]  Phil D. Green,et al.  Some solution to the missing feature problem in data classification, with application to noise robust ASR , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[31]  Svein Nordbotten.  EDITING STATISTICAL RECORDS BY NEURAL NETWORKS , 1995 .

[32]  S. Hanson,et al.  Some Solutions to the Missing Feature Problem in Vision , 1993 .

[33]  Seppo Laaksonen.  Regression-based nearest neighbour hot decking , 2000, Comput. Stat..

[34]  A. Macfarlane,et al.  Identifying problems with data collection at a local level: survey of NHS maternity units in England , 1999, BMJ.

[35]  Lynette A. Hunt,et al.  Mixture model clustering for mixed data with missing information , 2003, Comput. Stat. Data Anal..

[36]  Joop J. Hox,et al.  A review of current software for handling missing data , 1999 .

[37]  Ito Wasito,et al.  Nearest neighbour approach in the least-squares data imputation algorithms , 2005, Inf. Sci..

[38]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[39]  Edward C. Malthouse,et al.  Nonlinear partial least squares , 1997 .

[40]  Thomas G. Dietterich,et al.  Locally Adaptive Nearest Neighbor Algorithms , 1993, NIPS.

[41]  David W. Aha,et al.  Feature Weighting for Lazy Learning Algorithms , 1998 .

[42]  Joseph L Schafer,et al.  Analysis of Incomplete Multivariate Data , 1997 .

[43]  Nicole A. Lazar,et al.  Statistical Analysis With Missing Data , 2003, Technometrics.

[44]  Henry Tirri,et al.  Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data. , 1998 .

[45]  K. Gabriel,et al.  Least Squares Approximation of Matrices by Additive and Multiplicative Models , 1978 .

[46]  Sandrine Dudoit,et al.  Applications of Resampling Methods to Estimate the Number of Clusters and to Improve the Accuracy of , 2001 .

[47]  I. Jolliffe.  Principal Component Analysis , 2002 .

[48]  S. F. Buck.  A Method of Estimation of Missing Values in Multivariate Data Suitable for Use with an Electronic Computer , 1960 .

[49]  A. Cohen,et al.  Finite Mixture Distributions , 1982 .

[50]  Roderick J. A. Little.  Regression with Missing X's: A Review , 1992 .

[51]  R. Little,et al.  Editing and Imputation for Quantitative Survey Data , 1987 .

[52]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[53]  Yoshua Bengio,et al.  Recurrent Neural Networks for Missing or Asynchronous Data , 1995, NIPS.

[54]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .

[55]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[56]  Rich Caruana,et al.  A Non-Parametric EM-Style Algorithm for Imputing Missing Values , 2001, AISTATS.

[57]  Charu C. Aggarwal,et al.  Mining massively incomplete data sets by conceptual reconstruction , 2001, KDD '01.

[58]  Boris G. Mirkin,et al.  Least-Squares Structuring, Clustering and Data Processing Issues , 1998, Comput. J..

[59]  Michael E. Tipping,et al.  Mixtures of Principal Component Analysers , 1997 .

[60]  Peggy R. Wright,et al.  THE SIGNIFICANCE OF THE MISSING DATA PROBLEM IN KNOWLEDGE DISCOVERY , 1998 .

[61]  S. Zamir,et al.  Lower Rank Approximation of Matrices by Least Squares With Any Choice of Weights , 1979 .

[62]  Roderick J. A. Little,et al.  Modeling the Drop-Out Mechanism in Repeated-Measures Studies , 1995 .

[63]  Bradley Efron,et al.  Missing Data, Imputation, and the Bootstrap , 1994 .

[64]  R. Manne,et al.  Missing values in principal component analysis , 1998 .

[65]  Richard Dybowski.  Classification of incomplete feature vectors by radial basis function networks , 1998, Pattern Recognit. Lett..

[66]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[67]  D. Holt,et al.  A Systematic Approach to Automatic Edit and Imputation , 1976 .

[68]  B. Efron.  Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation , 1983 .

[69]  Gene H. Golub,et al.  Matrix computations , 1983 .

[70]  Xiao-Li Meng,et al.  Missing Data: Dial M for ??? , 2000 .

[71]  D Grzybowski.  Simplified deficiency processing brings hospital-wide benefits. , 2000, Journal of AHIMA.

[72]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[73]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[74]  R. Little,et al.  Maximum likelihood estimation for mixed continuous and categorical data with missing values , 1985 .

[75]  I. J. Good,et al.  Some Applications of the Singular Decomposition of a Matrix , 1969 .

[76]  B. Francis,et al.  Algorithmic approaches for fitting bilinear models. , 1992 .