Technical Reports

Technical Reports (1980 – 2011)

This is an archive listing of Technical Reports produced in the Department during the period from 1980 to 2011, at which point our Technical Report series was discontinued. Some of the Technical Reports are available online below. For other Technical Reports and for more recent research papers, see the author’s homepage, or visit the research overview page, or contact the author directly.


  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


  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


  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


  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


  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
  9. Evans-Shakhatreh-Optical Properties of Some Bayesian Infereces
  10. Evans-Comment on Bayesian Checking of the Second levels of Hierarchical Models


  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 – Efficent 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 uniquenesss 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


  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 estimation equation approach to longitudial 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


  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 Computing Risks Model with and without masking
  5. Roberts/Rosenthal/Sousa- Extremal Indices, Geomaetric 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/Gutman/Swartz -Relative Surprise Inferences and Computaitons for a Reliability Problem
  9. Srivastava-Some Tests Criteria for the Covariance Matrix with Fewer Observations Than the Dimension
  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 Craiu-Lee- Model Selection for the Computing Risks Model with and without masking
  13. Evans/Moshonov – Checking for Prior-Data Conflict


  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 – Scalling Limits for the Translent Phase of Local Metropolis-Hasitngs 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


  1. Evans/Zou – On the Robustness of relative suprise 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
  7. Chen/Hoppe/Iyengar/Brent – A hybrid logistic model fo case-control studies


  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 – Combinatiorlal 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


  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/Rosenthal – A note on geometric ergodicity and floating-point roundoff error
  5. Neal – Slice Sampling
  6. Alkhamisi/Fraser -On higher order likelihood analysis o 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-tiem Geometric Bounds to Convergence Rate of Markov Process on R
  12. Glimm/Srivastava – Multivariate Tests of normal mean vectors with restricted Alternatives
  13. Kollo/Srivastava -A new class of skewed multivariate distributions


  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 heirarchies
  7. Srivastava – Singular Wishart and Multivariate Beta Distributions
  8. Srivastava/Solanky – Predicitng Multivaritate Response in Linear Regression Model
  9. Jain/Rao – Computational procededure for the stead-state analysis of a finite-capacity-bulk service doule-ended queueing system
  10. Neal – Circulary-Coupled Markov Chain Sampling
  11. Israel/Rosenthal/Wei – Finding generators for Markov chains via empirical transition matrices


  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/West – Computing Bayesian Nonparametiric Hierarchical Models
  4. Fujikoshi/Seo -Asymptotic Expansion For The Joint Distribution of Correlated Hotelling’s T Statistics Under Normality
  5. Neal/Annealed Importance Sampling
  6. Srivastava/von Rosen – Growth Curve Models
  7. Srivastava-Aoshima – Classification With A Preassigned Error Rate When Two Covariance Matrices are Equal
  8. Gibbs -Bounding Convergence Time fo the Gibbs Sampler in Bayesian Image Restoration
  9. Petrone/Roberts – 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 cnditions for a sequence with negative drift to be uniformaly bounded in L
  15. Neal – Markov Chain Sampling Methods for Dirichlet Process Mixture Models
  16. Srivastava/Kubokawa – Improved Nonnegative Estimation of Multivariate Components of Varieance
  17. Srivastava/Kubokawa – Improved Nonnegative Estimation of Multivariate Components of Varieance
  18. Roberts/Rosenthal – Sufficient Markov Chain


  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 samples
  4. Efron/Tibshirani – The Problem of Regions
  5. Robersts/Rosenthal – Markov chain Monte Carlo-Some practical implications of theoretical results
  6. Srivastava – Resampling Methods for Imputing Missing Observations in Regression Models
  7. Srivastava – Resampling Methods for Imputing Missing Observations
  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 Convergebce rates of Gibbs samplers foruniform distribution
  12. Roberts/Rosenthal – Convergence of slice sampler Markov chains
  13. Nagao/rivastava – Fixed Width Confidence Region for The Mean of A Multivaritate Normal Distribution
  14. Kubokawa/Srivastava – Robust Improvements in Estimation of Mean and Covariance Matrices in Elliptically Contoured Distribution
  15. Fujikoshi/Seo – Asymptotic Approximations for EPMC’s of the Linear and the Quadratic Disciminant Functions When the Sample Sizes and the Dimension and Large
  16. Knight – Asymptotics for L Regression estimators under general conditions
  17. Yuen – Applications of Cheeger’s Constant to the Convergence Rate of Markov Chains on R
  18. Srivastava -Generalized Multivariate Analysis of Variance Models
  19. Evans/Swartz – An Algorithm fof the Approximation of Integrals with Exact Error bounds
  20. Oyet/Wiens – Robust Designs for Wavelet Approximations
  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/Srivastava – Testing Equality of Means and Simultaneus Confidence Intervals in Repeated Measures with Missing Data
  24. Cowles/Roberts/Rosenthal – Possible biases induced by MCMC convergence diagnostics


  1. Roberts-Rosenthal – Quantitative bonds for convergence rates of continuous time Markov process
  2. Tibshirani – Bias, variance and prediciton error for classification rules
  3. Hastie-Ikeda-Tibshirani – Computer-aided diagnosis of mammographic masses
  4. Cowles-Rosenthal – A simulation approach to convergence rates for Markov chain Monte Carlo allgorithms
  5. Redelmeier/Tibshirani – Cellular telephones and automobile collisions: some variations on matched case-control analysis
  6. Evans-Swartz – Ramdom Variable Generation Using Concavity Properties of Transformed Densities
  7. Neal-Dayan – Factor analysis using Delta-Rule Wake-Sleep learning
  8. Jain – Autoregressive progress 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 – Classified by Pairwise Coupling
  12. Fraser-Reid-Wu – A simple general formula for tail probabilities for frequentist and Bayesian inference


  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 chains now equals forever
  10. Evans-Swartz – Bayesian Integration Using Multivariate Student Important Sampling
  11. Srivastava – CUSUM procedure for Monitoring Variability
  12. Willmot-Lin- Bounds On The Tails of Convolutions of Compound Distributions
  13. Jain – a Comparison of sochastically Ordered Queues
  14. Roberts-Rosenthal-Schwartz – Convergence properties of perturbed Markov chains
  15. Tibshirani/Knight – Model search and inference by bootstrap “bumping”
  16. Kubokawa/Srivastava – Double Shrinkage Estimators of Ratio of Variances


  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


  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


  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


  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


  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


  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


  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


  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


  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


  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.


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


  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


  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


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


  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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