Technical Reports

Technical Reports

To see the Technical Reports produced in the Department during a particular year use the index for the relevant year below. Some Technical Reports are available online or from the author’s homepage. If a technical report is not available it can be obtained by writing to:

Technical Report Series
Department of Statistics
University of Toronto
Toronto, Ontario M5S 1A1

or by sending an email to christine@utstat.utoronto.ca

2012

Coming Soon

2011

1. M. Evans/Gun Ho Jang – Inferences from Prior-based Loss Functions
2. Zeynep Baskurt/M. Evans – Inequalities for Bayes Factors and Relative Belief Ratios

2010

  1. K. Łatuszyński/Rosenthal – Adaptive Gibbs sampler
  2. Thompson/Neal – Covariance-Adaptive Slice Sampling
  3. Cao/Evans/Guttman – Bayesian Factor Analysis via Concentration
  4. Evans/Jang – A Limit Result for the Prior Predictive
  5. Chen/Rosenthal – Decrypting Classical Cipher Text Using Markov Chain Monte Carlo
  6. Faye/Sun/Dimitromanolakis/Bull – A flexible genome-wide bootstrap method that accounts for ranking- and threshold-selection bias in GWAS interpretation and replication study design
  7. Thompson – A Comparison of Methods for Computing Autocorrelation Time
  8. Evans/Gilula/Guttman – Conversion of ordinal attitudinal scales: an inferential Bayesian approach
  9. Casarin/Craiu/Leisen – Interacting Multiple Try Algorithms with Different Proposal Distributions
  10. Thompson – Graphical Comparison of MCMC Performance
  11. Neal – MCMC Using Ensembles of States for Problems with Fast and Slow Variables such as Gaussian Process Regression

2009

  1. Bai – Simultaneous drift conditions for Adaptive Markov Chain Monte Carlo algorithms
  2. Craiu/Di Narzo – A Mixture-Based Approach to Regional Adaptation for MCMC
  3. Bai – An Adaptive Directional Metropolis-within-Gibbs algorithms
  4. Atchade/Roberts/Rosenthal – Optimal Scaling of Metropolis-Coupled Markov Chain Monte Carlo
  5. Proschan/Rosenthal – Beyond the Quintessential Quincunx
  6. Rosenthal/Yoon – Detecting Multiple Authorship of United States Supreme Court Legal Decisions Using Function Words
  7. Evans/Jang – Weak Informativity and the Information in One Prior Relative to Another

2008

  1. Yang – Recurrent and Ergodic Properties of Adaptive MCMC
  2. Yang – On The Weak Law Of Large Numbers For Unbounded Functionals For Adaptive MCMC
  3. Evans/Jang – Invariant P-values for Model Checking and checking for Prior-data Conflict
  4. Rosenthal – Optimal Proposal Distributions And Adaptive MCMC
  5. Rosenthal – Optimising Monte Carlo Search Strategies for Automated Pattern Detection
  6. Bai/Roberts/Rosenthal – On the Containment Condition for Adaptive Markov Chain Monte Carlo Algorithms
  7. Craiu/Rosenthal/Yang – Learn From Thy Neighbor: Parallel-Chain Adaptive MCMC
  8. Roberts/Rosenthal – Quantitative Non-Geometric Convergence Bounds for Independence Samplers
  9. Evans/Jang – The Information in One Prior Relative to Another

2007

  1. Rosenthal – Waiting Time Correlations on Disorderly Streetcar Routes
  2. Rosenthal – Notes About Markov Chain CLTs
  3. Rosenthal – AMCMC: An R interface For adaptive MCMC
  4. Hobert1/Rosenthal – Norm Comparisons for Data Augmentation
  5. Li/Zhang/Neal – A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
  6. Evans/Shakhatreh – Consistency of Bayesian Estimates for the Sum of Squared Normal Means with a Normal Prior
  7. Shahbaba/Neal – Nonlinear Models Using Dirichlet Process Mixtures
  8. Yao/Craiu/Reiser – Nonparametric Adjustment for Receiver Operating Characteristic Curves

2006

  1. Bedard – Weak Convergence of Metropolis Algorithms for Non-iid Target Distributions
  2. Bedard – Optimal Acceptance Rates for Metropolis Algorithms: Moving Beyond 0.234
  3. Jasra/Yang – A regeneration proof of the central limit theorem for uniformly ergodic Markov chains
  4. Srivastava – Some tests Criteria For the Covariance Matrix With Fewer Observations Than the Dimension
  5. Bedard – Efficient Sampling using Metropolis Algorithms: Applications of Optimal Scaling Results
  6. Shahbaba/Neal – Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors
  7. Neal – Puzzles of Anthropic Reasoning Resolved Using Full Non-indexical Conditioning
  8. Evans – Discussion of Nested Sampling for Bayesian Computations by John Skilling
  9. Staicu/Reid – On the uniqueness of probability matching priors
  10. Roberts/Rosenthal – Examples of adaptive MCMC
  11. Roberts/Rosenthal/Segers/Sousa – Extremal Indices, Geometric Ergodicity of Markov Chains, and MCMC
  12. Roberts/Rosenthal – Variance Bounding Markov Chains

2005

  1. Roberts/Rosenthal – Coupling and Ergodicity of Adaptive MCMC
  2. Srivastava/Kubokawa – Comparison of Discrimination Methods for High Dimensional Data
  3. Evans/Moshonov – Checking for Prior-Data Conflict with Hierarchically Specified Priors
  4. Craiu/Sun – Choosing the Lesser Evil: Trade-off Between False Discovery Rate and Non-Discovery Rate
  5. Bull/Lewinger/Lee – Penalized Maximum Likelihood Estimation for Multinomial Logistic Regression Using the Jeffreys Prior
  6. Neal – The Short-Cut Metropolis Methods
  7. Jain/Neal – Splitting and Merging Components of a Nonconjugate Dirichlet Process Mixture Model
  8. Evans/Guttman/Swartz – Optimality and Computations for Relative Surprise Inference
  9. Craiu/Duchesne – A Generalized Estimating Equations Approach to Longitudinal Conditional Logistic Regression
  10. Shahbaba/Neal – Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior
  11. Neal – Estimating Ratios of Normalizing Constants Using Linked Importance Sampling
  12. Jain – The GI/G/K/N queue with supplementary variable method

2004

  1. Srivastava – Multivariate Theory For Analyzing High Dimensional Data
  2. Roberts/Rosenthal – General State Space Markov Chains and MCMC Algorithms
  3. Bramson/Quastel/Rosenthal – When Can Martingales Avoid Ruin?
  4. Craiu/Lee – Model Selection for the Competing Risks Model with and without masking
  5. Roberts/Rosenthal/Sousa – Extremal Indices, Geometric Ergodicity Of Markov Chains, and MCMC
  6. Neal – Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better
  7. Craiu – Antithetic Acceleration of the Multiple-Try Metropolis
  8. Evans/Guttman/Swartz – Relative Surprise Inferences and Computations For a Reliability Problem
  9. Srivastava – Some Tests concerning the Covariance Matrix in High Dimensional Data
  10. Srivastava/Kubokawa – Empirical Bayes Regression Analysis with Many Regressors but Fewer Observations
  11. Neal – Taking Bigger Metropolis Steps by Dragging Fast Variables
  12. Roberts/Rosenthal – Harris Recurrence of Metropolis-Within-Gibbs and Trans-Dimensional Markov Chains
  13. Evans/Moshonov – Checking for Prior-Data Conflict

2003

  1. Craiu/Duchesne – Inference Based on the EM Algorithm for the Competing Risk Model with Masked Causes of Failure
  2. Roberts/Rosenthal – Downweighting Tightly Knit Communities in World Wide Web Rankings
  3. Christensen/Roberts/Rosenthal – Scaling Limits for the Transient Phase of Local Metropolis-Hastings Algorithms
  4. Neal – Markov Chain Sampling for Non-Linear State Space Models Using Embedded Hidden Markov Models
  5. Atchade/Rosenthal – On Adaptive Markov Chain Monte Carlo Algorithms
  6. Sun/Bull – Resampling-Based Testing and Effect Estimation in Genomewide Scans

2002

  1. Evans/Zou – On the Robustness of Relative Surprise Inferences to the Choice of Prior
  2. Duchesne/Rosenthal – Stochastic Justification of Some Simple Reliability Models*
  3. Rosenthal – Quantitative convergence rates of Markov chains: A simple account
  4. Feuerverger/Rosenthal – Achieving Limiting Distributions for Markov Chains Using Back Buttons
  5. Craiu/Meng – Multi-process Parallel Antithetic Coupling For Backward and Forward Markov Chain Monte Carlo
  6. Srivastava – Multivariate Analysis With Fewer Observations than the Dimension

2001

  1. Pinto/Neal – Improving Markov Chain Monte Carlo Estimators by Coupling to an Approximating Chain
  2. Bellhouse/Chipman/Stafford – Additive models for survey data via penalized least squares
  3. Drekic/Stafford – Symbolic Computation of Moments in Priority Queues
  4. Neal – Defining Priors for Distributions Using Dirichlet Diffusion Trees
  5. Roberts/Rosenthal – One-Shot Coupling for Cetain Stochastic Recursive Sequences
  6. Duchesne/Stafford – A kernel density estimate for interval censored data
  7. Roberts/Rosenthal – Combinatorial identities associated with CFTP
  8. Neal – Transferring Prior Information between Models Using Imaginary Data
  9. Roberts/Rosenthal – Optimal scaling for various Metropolis-Hastings algorithms
  10. Rosenthal – Asymptotic Variance and Convergence Rates of Nearly-Periodic MCMC Algorithms

2000

  1. Roberts/Rosenthal – Small and Pseudo-Small Sets for Markov Chains.
  2. Jain/Rao – State-Dependent Rates In A Finite-Capacity Double-Ended Queue With an Application To Inventory Problem.
  3. Jain/Neal – A Split-Merge Markov Chain Monte Carlo Procedure For the Dirichlet Process Mixture Model.
  4. Roberts/Breyer/Rosenthal – A note on geometric ergodicity and floating-point roundoff error
  5. Neal – Slice Sampling
  6. Alkhamisi/Fraser – On Higher Order Likelihood Analysis of The One-Way Random Effects
  7. Lu/Rosenthal/Shaffer – Crossword puzzles: Experiments with meta-search in propositional reasoning
  8. Gordon/Rosenthal – Capitalism’s Growth Imperative
  9. Borodin/Roberts/Rosenthal/Tsaparas – Finding Authorities and Hubs From Link Structures on the World Wide Web
  10. Srivastava – Nested Growth Curve Models
  11. Yuen – Generalization of Discrete-time Geometric Bounds to Convergence Rate of Markov Processes on $ R sup n $
  12. Glimm/Srivastava – Multivariate Tests of normal mean vectors with restricted Alternatives
  13. Kollo/Srivastava – A New Class of Skewed Multivariate Distributions

1999

  1. Hirotsu/Srivastava – Simultaneous Confidence Intervals Based on One-sided max t Test
  2. Rosenthal – Parallel computing and Monte Carlo algorithms
  3. Rosenthal – A review of asymptotic convergence for general state space Markov chains
  4. Roberts/Rosenthal – The Polar Slice Sampler
  5. Roberts/Rosenthal – Recent progress on computable bounds and the simple slice sampler
  6. Roberts/Rosenthal – Bayesian models with infinite hierarchies
  7. Srivastava – Singular Wishart and Multivariate Beta Distributions
  8. Srivastava/Solanky – Predicting Multivariate Response In Linear Regression Model
  9. Jain/Rao – Computational procedure for the steady-state analysis of a finite-capacity-bulk-service double-ended queueing system
  10. Neal – Circularly-Coupled Markov Chain Sampling
  11. Israel/Rosenthal/Wei – Finding generators for Markov chains via empirical transition matrices

1998

  1. Feuerverger/Robinson/Wong – On the Second Order Relative Accuracy of Certain Bootstrap and Saddlepoint Approximation Procedures
  2. Evans/Swartz – Higher Order Envelope Random Variate Generators
  3. Escobar $ sup 1 $/West – Computing Bayesian Nonparametric Hierarchical Models
  4. Fujkoshi/$ Seo sup * $ – Asymptotic Expansions For The Joint Distribution Of Correlated Hotelling’s $ T sup 2 $ Statistics Under Normality
  5. Neal – Annealed Importance Sampling
  6. Srivastava/von Rosen – Growth Curve Models
  7. Srivastava/Oashima – Classification With A Preassigned Error Rate When Two Covariance Matrices Are Equal
  8. Gibbs – Bounding Convergence Time of the Gibbs Sampler in Bayesian Image Restoration
  9. Petrone/Roberts/Rosenthal – A note on convergence rates of Gibbs sampling for nonparametric mixtures*
  10. Murdoch/Rosenthal – Efficient Use of Exact Samples
  11. Osborne/Rosenthal/Tanner – Meeetings with costly participation
  12. Murdoch/Rosenthal – An extension of Fill’s exact sampling algorithm to non-monotone chains
  13. Jain/Reiss – Busy Periods and Busy Cycles In Bulk-Arrival Queueing Systems
  14. Pemantle/Rosenthal – Moment conditions for a sequence with negative drift to be uniformly bounded in $ L sup r $
  15. Neal – Markov Chain Sampling Methods for Dirichlet Process Mixture Models
  16. Srivastava/Kubokawa – Improved Nonnegative Estimation of Multivariate Components of Variance
  17. Kubokawa/Srivastava – Estimating Risk and Mean Squared Error Matrix in Stein Estimation
  18. Roberts/Rosenthal – Sufficient Markov Chain

1997

  1. Pavlenko – Asymptotic behavior of the probabilities misclassification for discriminant functions with weighting
  2. Neal – Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
  3. Roberts/Rosenthal – Two convergence properties of hybrid samplers
  4. Efron/Tibshirani – The Problem of Regions
  5. Roberts/Rosenthal – Markov chain Monte Carlo: Some practical implications of theoretical results
  6. Srivastava – Resampling Methods for Imputing Missing Observations
  7. Resampling Methods for Imputing Missing Observations in Regression Models
  8. Rosenthal/Schwartz – Gambling Systems and Multiplication-Invariant Measures
  9. Zarepour/Knight – Bootstrapping point processes with some applications
  10. Andrews/Austin/Quigley – Measuring Warehouse Performance
  11. Roberts/Rosenthal – On convergence rates of Gibbs samplers for uniform distributions
  12. Roberts/Rosenthal – Convergence of slice sampler Markov chains
  13. Nagao/Srivastava – Fixed Width Confidence Region for The Mean of A Multivariate Normal Distribution
  14. Kubokawa/Srivastava – Robust Improvements in Estimation of Mean and Covariance Matrices in Elliptically Contoured Distribution
  15. Fujikoshi/ $ Seo sup 1 $ – Asymptotic Approximations for EPMC’s of the Linear and the Quadratic Discriminant Functions When the Sample Sizes and the Dimension are Large
  16. Knight – Asymptotics for $ L sub 1 $ regression estimtors under general conditions
  17. $ Yuen sup * $ – Applications of Cheeger’s Constant to The Convergence Rate of Markov Chains on $ R sup n $
  18. Srivastava – Generalized Multivariate Analysis of Variance Models
  19. Evans/Swartz – An Algorithm For The Approximation Of Integrals With Exact Error Bounds
  20. Oyet/Wiens – Robust Designs for Wavelet Approximations of Regression Models
  21. Tibshirani/Knight – The covariance inflation criterion for adaptive model selection
  22. Neal – Markov Chain Monte Carlo Methods Based on `Slicing’ the Density Function
  23. $ Seo sup * $ /Srivastava – Testing Equality of Means and Simultaneous Confidence Intervals in Repeated Measures with Missing Data
  24. Cowles/Roberts/Rosenthal – Possible biases induced by MCMC convergence diagnostics

1996

  1. Roberts/Rosenthal – Quantitative bounds for convergence rates of continuous time Markov processes
  2. Tibshirani – Bias, variance and prediction error for classification rules
  3. Haste/Ikeda/Tibshirani – Computer-aided diagnosis of mammographic masses
  4. Cowles/Rosenthal – A simulation approach to convergence rates for Markov chain Monte Carlo algorithms
  5. Redelmeier/Tibshirani – Cellular telephones and automobile collisions: some variations on matched case-control analysis
  6. Evans/Swartz – Random Variable Generation Using Concavity Properties of Transformed Densities
  7. Neal/Dayan – Factor Analysis Using Delta-Rule Wake-Sleep Learning
  8. Jain – Autoregressive process and its Applications To Queueing Model
  9. Roberts/Rosenthal – Geometric Ergodicity and Hybrid Markov Chains
  10. Tibshirani – Who is the fastest man in the world?
  11. Hastie/Tibshirani – Classification by Pairwise Coupling
  12. Fraser/Reid/Wu – A simple general formula for tail probabilities for frequentist and Bayesian inference

1995

  1. Srivastava – Robustness of Control Procedures For Integrated Moving Average Provess of Order One
  2. Willmot/Lin – Bounds On The Tails of Convolutions Of Compound Distributions
  3. Srivastava/Wu – Evaluation of Optimum Weights and Average Run Lengths in EWMA Control Schemes
  4. Zarepour/Knight – Bootstrapping unstable first order autoregressive processes with errors in the domain of attraction of stable law
  5. Reid – Higher order asymptotics and likelihood: a review
  6. Reid – Statistics in the twenty-first century: Asymptotic theory and the foundations of statistics
  7. Roberts/Rosenthal – Optimal scaling of discrete approximations to Langevin diffusions
  8. Neal – Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation
  9. Rosenthal – Faithful couplings of Markov chain: now equals forever
  10. Srivastava – Reduced Rank Discrimination
  11. Evans/Swartz – Bayesian Integration Using Multivariate Student Importance Sampling
  12. Srivastava – CUSUM Procedure for Monitoring Variability
  13. Willmot/Lin – Simplified Bounds on the Tails of Compound Distributions
  14. Jain – A Comparison of Stochastically Ordered Que
  15. Roberts/Rosenthal/Schwartz – Convergence properties of perturbed Markov chains
  16. Tibshirani/Knight – Model search and inference by bootstrap “bumping”
  17. Kubokawa/Srivastava – Double Shrinkage Estimators of Ratio of Variances

1994

  1. Hastie/Tibshirani- Discriminant Analysis by Gaussian Mixtures
  2. Tibshirani – Regression shrinkage and selection via the Lasso
  3. Reid – The roles of conditioning in inference
  4. Oman/Srivastava – Exact Mean Squared Error Comparisons of the Inverse and Classical Estimators in Multi-univariate Linear Calibration
  5. Lin – Tail of Compound Distributions and Excess Time
  6. Baxter/Rosenthal – Rates of Convergence for Everywhere-Positive Markov Chains
  7. Srivastava – Admissibility of the Inverse and the Inadmissibility of the Classical Estimators in Multi-univariate Linear Calibration
  8. Boyle/Lin – Optimal Portfolio Selection With Transaction Costs
  9. Tibshirani – A proposal for variable selection in the Cox model
  10. Tibshirani – A comparison of some error estimates for neural network models
  11. Jain – Diffusion Approximation and Estimation for G/G/1 Queueing Systems
  12. Banjevic – Recurrent Relations for Distribution of Waiting Time in Markov Chain
  13. Rosenthal – Analysis of the Gibbs sampler for a model related to James-Stein estimators
  14. Mojirsheibani/Tibshirani – Bootstrap Prediction Intervals For a Future MLE
  15. Tibshirani/Hinton – “Coaching” variables for regression and classification
  16. Evans/Swartz – Methods for Approximating Integrals With Applications to Statistics
  17. Jain – Problem of Statistical Inference for Heavy Traffic in M/M/1 Queue
  18. Jain – Sequential Probability Ratio Test to Control the Traffic Intensity for M/M/1 Queueing Model
  19. Evans – Bayesian Hypothesis Testing via the Concept of Surprise
  20. LeBlanc/Tibshirani – Monotone Shrinkage of Trees
  21. Neal – Sampling from Multimodal Distributions Using Tempered Transitions
  22. Roberts/Rosenthal – Shift-coupling and convergence rates of ergodic averages
  23. Rosenthal – Markov chain convergence: from finite to infinite
  24. Abdolell/LeBlanc/McLaughlin – Poisson Regression Trees

1993

  1. Guttman/Olkin/Philips – Estimating The Number Of Aberrant Laboratories
  2. Tang – Selection Of U-Designs
  3. Fraser/Reid – Ancillaries and third order significance
  4. Jing/Feuerverger/Robinson – Saddlepoint Approximations in Bootstrap Applications
  5. Rao/Tibshirani – Bootstrap Model Selection Via The Cost Complexity Parameter In Regression
  6. Leblanc/Crowley – Step-function Covariate Effects in the Proportional Hazards Model
  7. LeBlanc – An Adaptive Expansion Method for Regression
  8. Andrews/Feuerverger – General Saddlepoint Approximation Methods for Bootstrap Configurations
  9. Berhane/Tibshirani – Generalized Additive Models for Longitudinal Data
  10. Mojirsheibani/Tibshirani – Bootstrap Prediction Intervals for a Future MLE
  11. Evans/Guttman/Haitovsky/Swartz – Bayesian Analysis of Binary Data Subject to Misclassification
  12. Kim – Group Representations and Nonparametric Density and Deconvolution Estimation on Compact Lie Groups
  13. Srivastava – Economical Quality Control Procedures Based on Integrated Moving Average Process of Order One
  14. Yao/Tritchler – Directed Acyclic Graphs, Linear Recursive Regression, and Inference about Causal Ordering
  15. Evans/Gilula/Guttman/Swartz – Bayesian Analysis of Stochastically Ordered Distributions of Categorical Variables
  16. LeBlanc/Tibshirani – Combining estimates in regression and classification
  17. Healy/Kim – An Empirical Bayes Approach to Directional Data and Efficient Computation on the Sphere*
  18. Rosenthal – Markov Chains, Eigenvalues, and Coupling
  19. Rosenthal – Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
  20. Rosenthal – Rates of Convergence for Gibbs Sampling for Variance Component Models

1992

  1. Evans – The surprise distribution and some uses in statistical inference
  2. Srivastava/Chow – Fast Accurate Approximations for the ARLs of the FIR CUSUM Scheme and a Simple Method to Calculate the Decision Boundary for the CUSUM Scheme
  3. Srivastava/Wu – On-line Quality Control Procedures based on Random Walk Model and Integrated Moving Average Model of Order (0,1,1)
  4. Guttman/Pena – A Bayesian Look At Diagnostics In The Univariate Linear Model
  5. Wang – Smoothing Splines for Non-parametric Regression Percentiles
  6. Pena/Guttman – Comparing Probabilistic Methods for Outlier Detection
  7. Reiser/Guttman/Lin/Guess/Usher – Bayesian Inference for Masked System Life Time Data
  8. Srivastava/Chow – Comparison of the CUSUM Procedure with Other Procedures that Detect an Increase in the Variance and a Fast Accurate Approximation For the ARL of the CUSUM Procedure
  9. Hastie/Buja/Tibshirani – Penalized Discriminant Analysis
  10. Hastie/Tibshirani – Handwritten Digit Recognition via Deformable Prototypes
  11. Guttman/Pena – A Bayesian Look At Diagnostics In The Univariate Linear Model
  12. Lin/Guttman – Handling spuriosity in the Kalman filter
  13. Mo/Wang – Asymptotic Normality for Estimators of Eigenvectors
  14. Srivastava/Chow – A Comparison of Some OMNIBUS CUSUM and OMNIBUS EWMA Statistical Process Control Procedures

1991

  1. Lin/Guttman – Handling Spuriosity in the Kalman Filter
  2. Tibshirani/LeBlanc – A Strategy for Binary Classification and Description
  3. O’Rourke/Naylor/McGeer/L’Abbe/Detsky – Incorporating Quality Appraisals into Meta-analyses of Randomized Clinical Trials
  4. Guttman – A Bayesian Look At The Question Of Diagnostics
  5. Andrews/Stafford – Tools for the Symbolic Computation of Asymptotic Expansions
  6. Brunner – Bayesian nonparametric methods for data from a unimodal density
  7. DiCiccio/Tibshirani – On the implementation of profile likelihood methods
  8. Hastle/Tibshirani – Varying-coefficient models
  9. Mo – Sensitivity Analysis For Additive Regression And Its By-products
  10. Tibshirani/LeBlanc – A Strategy for Binary Classification and Description;
  11. O’Rourke/Naylor/McGeer/L’Abbe/Detsky – Incorporating Quality Appraisals into Meta-analyses of Randomized Clinical Trails
  12. Guttman/Olkin – A Model For Estimating The Number of Aberrant Laboratories
  13. Lin/Guttman – Handling Spuriosity in the Kalman Filter
  14. Srivastava/Wu – On Taguchi’s On-Line Control Procedure With Measurement Error
  15. LeBlanc/Tibshirani – Adaptive Principal Surfaces
  16. Mo – Nonparametric Estimation by (Parametric) Linear Regression
  17. Tibshirani – Principal Curves Revisited
  18. Mo – Asymptotic Normality of Minimum Contrast Estimators
  19. Evans/Guttman/Olkin – Numerical Aspects In Estimating The Parameters Of A Mixture Of Normal Distributions;
  20. Chen – Extended Quasi-likelihoods and Optimal Estimating Functions
  21. Chen – Quasi-likelihood Estimation in Stochastic Regression Models
  22. Srivastava/Wu – An Improved Version of Taguchi’s On-line Control Procedure;
  23. Srivastava/Wu – Taguchi’s On-line Control Procedures and Some Improvements;
  24. Srivastava/Wu – A Comparison of EWMA and CUSUM Procedures in the Two-sided Case
  25. Srivastava/Wu – Dynamic Sampling Plan in CUSUM Procedure for Detecting a Change in the Drift of Brownian Motion
  26. Srivastava/Wu – Dynamic Sampling Plan in Shiryayev-Roberts Procedure for Detecting a Change in the Drift of Brownian Motion

1990

  1. Srivastava/Wu – Optimal Bayes search for the change point in a finite interval
  2. Wong/Reid – Solutions to Selected Exercises/Analysis of Survival Data
  3. Srivastava/Wu – A second order approximation on Taguchi’s on-line control procedure
  4. Fraser/Reid – From multiparameter likelihood to tail probability for a scalar parameter
  5. Andrews – Calculations with Random Variables using Mathematica
  6. Brant/Tibshirani – Missing covariate values in generalized regression models
  7. Evans – Adaptive Importance Sampling and Chaining
  8. Evans/Swartz – Inferential and Computational Uses of a Class of Density Functions
  9. Efron/Tibshirani – Statistical Data Analysis In The Computer Age
  10. Evans/Gilula/Guttman – Log-Linear And Goodman’s RC Model
  11. Reiser/Faraggi/Guttman – Choice of Sample Size for Stress-Strength Models
  12. Draper/Guttman – Treating Bias as Variance for Experimental Design Purposes
  13. Mo – Robust Additive Regression I: Population Aspect
  14. Mo – Robust Additive Regression II: Finite Sample Approximations
  15. Srivastava/Wu – On Beta-Binomial Model for Extrabinomial Variation
  16. Srivastava/Wu – Comparison of Cusum, Ewma, and Shiryayev-Roberts Procedures for Detecting A Shift In The Mean

1989

  1. Tibshirani – Smoothing Methods For The Study of Synergism
  2. Bell/Reid – Statistical Problems in Rainfall Measurements From Space
  3. Draper/Guttman – Rationalization of The “Alphabetic-Optimal” and “Variance Plus Bias” Aproaches to Experimental Desin
  4. Srivastava/Khan – Multivariate Cusum Procedures for The Normal Mean Vector
  5. Guttman/Bagchi – Prediction In Circular Distributions
  6. Keen/Srivastava – The Asymptotic Variance of the Interclass Correlation Coefficient
  7. Lin/Chen – On The Identity Relationships of $ 2 sup { k-p } $ Designs
  8. Srivastava/Wu – Optimal Bayes Stopping Rules for Detecting the Change Point In A Bernoulli Process
  9. Srivastava/Wu – Change Point Problem In A Diffusion Process With Partial Observations
  10. Bagchi/Draper/Guttman – Bayesian Assessment of Assumptions of Regression Analysis
  11. Guttman/Olkin – Modeling Interlaboratory Differences: A Bayesian Analysis
  12. Srivastava/Wu – Statistical Inference and Optimal Inspection with Incomplete Inspections
  13. Srivastava/Wu – Optimal Bayes Stopping Rules for Detecting the Change Point In A Bernoulli Process
  14. Srivastava/Wu – Change Point Problem In A Diffusion Process With Partial Observations
  15. Bagchi/Draper/Guttman – Bayesian Assessment of Assumptions of Regression Analysis
  16. Guttman/Olkin – Modeling Interlaboratory Differences: A Bayesian Analysis
  17. Srivastava/Wu – Statistical Inference and Optimal Inspection with Incomplete Inspections
  18. Bhattacharyya/Johnson/Guttman/Reiser – Bayesian Inference for Stress-Strength Models with Explanatory Variables
  19. Brunner – Bayesian linear regression with error terms that have symmetric unimodal densities
  20. Keen/Srivastava – The Asymptotic Variance of the Interclass Correlation Coefficient

1988

  1. Fraser – Normed Likelihood as Saddlepoint Approximation
  2. Evans – An Example Concerning the Likelihood Function
  3. Fraser/Reid – On Conditional Inference for a Real Parameter: a Differential Approach on the Sample Space
  4. Tibshirani and Hastie – Exploring the nature of covariate effects in the proportional hazards model
  5. Andrews – General Monte Carlo Methods for Research in Statistics
  6. Bagchi/Guttman – Spuriosity and Outliers in Circular Data
  7. Bagchi/Draper/Guttman – Bayesian Assessment of Assumptions of Regression Analysis
  8. Feuerverger – On the Empirical Saddlepoint
  9. McCullagh/Tibshirani – A simple method for the adjustment of profile likelihoods
  10. Fraser/Reid – Adjustments to profile likelihood
  11. Evans – Monte Carlo Computation of Marginal Posterior Quantiles
  12. Tibshirani – Non-informative priors for one parameter of many
  13. Guttman/Menzefricke – Bayesian Estimation in Two-Way Tables with Heterogeneous Variances
  14. Evans – Chaining via anealing
  15. Srivastava/Ng – Comparison of the Estimators of Intraclass Correlation in The Presence of Covariables
  16. Srivastava/Yau – Tail Probability Approximations of a General Statistic With Application to Durbin-Watson Statistic
  17. Yau/Srivastava – Approximation of tail probability of a linear combination of non-central chi-squares by saddlepoint method
  18. Evans/Gilula/Guttman – Latent Class Analysis of Two-Way Contingency Tables by Bayesian Methods
  19. Srivastava/Yau – Saddlepoint method for obtaining tail probability of Wilk’s likelihood ratio test
  20. Bilodeau – How should one choose the loss function to estimate the covariance structure of a generalized linear model?
  21. Reiser/Guttman – Sample Size Choice For Strength Stress Models
  22. Tibshirai/Wasserman – Some aspects of the reparameterization of statistical models
  23. Pena/Guttman – Optimal collapsing of mixture distributions in robust recursive estimation

1987

  1. Srivastava/Keen/Katapa – Estimation of Interclass and Intraclass Correlations in Multivariate
  2. Srivastava – Testing for Block Effects and Analysis of Regression Models Based Testing
  3. Srivastava/Bilodeau – Stein Estimation Under Elliptical Distributions
  4. Hastie/Tibshirani – Generalized Additive Models, Cubic Splines and Penalized Likelihood
  5. Reid – Saddlepoint Methods and Statistical Inference, Revised
  6. Srivastava/Keen – Monte Carlo Comparisons of Bootstrap Methods
  7. Srivastava/Keen – Point and Interval Estimation of the Intraclass Correlation Coefficient
  8. Manchester/Trueman – Duchen I: An Interactive Computer Program for Calculating Risks in X
  9. Bagchi/Guttman – Bayesian Regression Analysis under Non-Normal Errors
  10. Buja/Hastie/Tibshirani – Linear Smoothers and Additive Models
  11. Srivastava/Keen – Multivariate Intraclass & Interclass Correlations
  12. Wasserman – Prior Envelopes Based on Belief Functions
  13. Tibshirani – Variance Stabilization and the Bootstrap
  14. Feuerverger – The Analysis of Linear and Nonlinear Time Series by Independence – Testing Procedures
  15. Feuerverger/McLeish/Rubinstein – Sensitivity Analysis, the “What If” Problem, and Simulation of Queueing Networks in Heavy Traffic
  16. Feuerverger – Some New Perspectives on the MLE and LRT
  17. Guttman/Bagchi – Theoretical Considerations of the Multivariate Von Mises-Fisher Distribution

1986

  1. T. DiCiccio/R. Tibshirani- Approximating the Profile Likelihood Through Stein’s Least Favourable Family
  2. M.S. Srivastava -Bootstrap Method in Ranking Slippage Problems 1,2
  3. A. Dobriyal/D.A.S. Fraser – Linear Calibration – A Fiductial Method for Interval Estimation
  4. R. Tibshirani – Estimating Transformations for Regression – A Variation on ACE
  5. I. Guttman/U. Menzefricke – Bayesian Power
  6. R. Tibshirani/L. Wasserman – Non Resistent Parameters
  7. M. Evans/T. Swartz – Monte Carlo Computation of Some Multivariate Normal Probabilities
  8. Bhatt/Guttman/Johnson/Reiser – Statistical Inference for Stress-Strength Models With Covariates
  9. N.Draper/M.Evans/I.Guttman – A Bayesian Approach To System Reliability When Two Components Are Dependent
  10. Guttman/Draper – Model Selection Problems
  11. S. Chakravorti/I. Guttman – A Large Sample Analysis of the Magnitudinal Model in Multivariate Analysis
  12. R. Tibshirani – Estimating Transformation for Regression

1985

  1. I. Guttman/D. Pena – Robust Kalman Filtering and its Applications
  2. D.A.S. Fraser/R.J. Gebotys – Non-Nested Linear Models: A Conditional Confidence Approach
  3. R. Tibshirani – How Many Bootstraps?
  4. B. Efron/R. Tibshirani – The Bootstrap Method for Assessing Statistical Accuracy
  5. M.S. Srivastava – Bootstrapping Durbin-Watson Statistics
  6. M.S. Srivastava – Bootstrapping in Ranking and Slippage Problems
  7. Y.M. Chan/M.S. Srivastava – Robustness ofFieller’s Theorem & Comparison with Bootstrap Method
  8. R. Tibshirani/L. Wasserman – A Note on Profile Likelihood, Least Favourable Families and Kullback-Leibler Distance
  9. I. Guttman/M.S. Srivastava – Bayesian Method of Detecting Change Point in Regression and Growth Curve Models
  10. I. Guttman/U. Menzefricke/D. Tyler – Magnitudinal Effects in the Normal Multivariate Model
  11. S.A. Bartlett/I. Guttman – Predictive and Posterior Distributionns for Normal Multivariate Data With Missing Monotone Patterns.
  12. M. Evans/D.A.S. Fraser/G. Monette – On the Sufficiency-Conditionality to Likelihood Argument
  13. M. Evans/T. Swartz – Sampling from Gauss Rules
  14. T. Hastie/R. Tibshirani – Generalized Additive Models
  15. T. DiCiccio/R. Tibshirani – Bootstrap Confidence Intervals & Bootstrap Approximations
  16. T. Hastie/R. Tibshirani – Generalized Additive Models: Some Applications
  17. B. Reiser/I. Guttman – A Comparison of Three Point Estimators for P(Y lt X):The Normal Case
  18. B. Reiser/I. Guttman – Statistical Inference for P(Y lt X) – The Normal Case
  19. M.S. Srivastava – Multivariate Bioassay, Combination of Bioassays, and Fieller’s Theorem
  20. Y.M. Chan/M.S. Srivastava – Comparison of Powers for the Sphericity Tests Using Both the Asymtotic Distribution and the Bootstrap Method.
  21. M. Bilodeau/M.S. Srivastava – Stein Estimators Under Elliptical Distributions
  22. M.S. Srivastava/Y.M. Chan – A Comparison of Bootstrap Method and Edgeworth Expansion in Approximating The Distribution of Sample Variance — One Sample and Two Sample Cases.

1984

  1. I. Guttman/P. Hougaar – Studentization and Prediction Problems in Multivariate Multiple Regression

1983

  1. M.S. Srivastava/T.K. Hui – Tests for Multivarate Normality Based on Multivariate Skewness and Kurtosis
  2. H. Niederhausen – Some Problems Connected with the Number of Records in a Sequence of Observations
  3. H. Niederhausen – Sequences of Binomial Type with Polynomial Coefficients

1982

  1. M.S. Srivastava/G.C. Lee – On the Robustness of Tests for Correlation Coefficient in the Presence of an Outlier.
  2. M.S. Srivastava/G.C. Lee – On the Choice of Transformations of the Correlation Coefficient With or Without an Outlier.
  3. I. Guttman/N.R. Draper – Dropping Observations Without Affecting Posterior and Predictive Distributions
  4. I. Guttman/U. Menzefricke – Bayesian Inference in Multivariate Regression with Missing Observations on the Response Variables.
  5. M.S. Srivastava – A Graphical Method for Assessing Multivarate Normality and a Measure of Skewness and Kurtosis.
  6. M.S. Srivastava/T.K. Hui – Measures of Multivariate Skewness & Kurtosis

1981

  1. Dahiya/Guttman – Shortest Confidence and Prediction Intervals for the Log-normal.
  2. Chikara/Guttman – Tolerance for the Inverse Gaussian Distribution

1980

  1. Srivastava/Carter – Asymptotic Distribution of Latent Roots and Applications
  2. Srivastava – Multivariate Data with Missing Observations
  3. Srivastava – On Tests for Detecting Change in the Multivariate Mean
  4. Srivastava/Awan – On the Robustness of Hotelling’s T2-test and Distribution of Linear and Quadratic Forms in Sampling from a Mixture of Two Multivariate Normal
  5. Waugh – Application of the Galton-Watson Process to the Kin Number Problem
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