## 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.

### 2011

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

### 2010

- K. Łatuszyński/Rosenthal – Adaptive Gibbs sampler
- Thompson/Neal – Covariance-Adaptive Slice Sampling
- Cao/Evans/Guttman – Bayesian Factor Analysis via Concentration
- Evans/Jang – A Limit Result for the Prior Predictive
- Chen/Rosenthal – Decrypting Classical Cipher Text Using Markov Chain Monte Carlo
- 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
- Thompson – A Comparison of Methods for Computing Autocorrelation Time
- Evans/Gilula/Guttman – Conversion of ordinal attitudinal scales: an inferential Bayesian approach
- Casarin/Craiu/Leisen – Interacting Multiple Try Algorithms with Different Proposal Distributions
- Thompson – Graphical Comparison of MCMC Performance
- Neal – MCMC Using Ensembles of States for Problems with Fast and Slow Variables such as Gaussian Process Regression

### 2009

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

### 2008

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

### 2007

- Rosenthal – Waiting Time Correlations on Disorderly Streetcar Routes
- Rosenthal – Notes About Markov Chain CLTs
- Rosenthal – AMCMC: An R interface For adaptive MCMC
- Hobert
^{1}/Rosenthal – Norm Comparisons for Data Augmentation
- Li/Zhang/Neal – A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
- Evans/Shakhatreh – Consistency of Bayesian Estimates for the Sum of Squared Normal Means with a Normal Prior
- Shahbaba/Neal – Nonlinear Models Using Dirichlet Process Mixtures
- Yao-Craiu-Reiser- Nonparametric Adjustment for Receiver Operating Characteristic Curves
- Evans-Shakhatreh-Optical Properties of Some Bayesian Infereces
- Evans-Comment on Bayesian Checking of the Second levels of Hierarchical Models

### 2006

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

### 2005

- Roberts/Rosenthal – Coupling and Ergodicity of Adaptive MCMC
- Srivastava/Kubokawa – Comparison of Discrimination Methods for High Dimensional Data
- Evans/Moshonov – Checking for Prior-Data Conflict with Hierarchically Specified Priors
- Craiu/Sun – Choosing the Lesser Evil Trade-off Between False Discovery Rate and Non-Discovery Rate
- Bull/Lewinger/Lee – Penalized Maximum Likelihood Estimation for Multinomial Logistic Regression Using the Jeffreys Prior
- Neal – The Short-Cut Metropolis Methods
- Jain/Neal – Splitting and Merging Components of a Nonconjugate Dirichlet Process Mixture Model
- Evans/Guttman/Swartz – Optimality and Computations for Relative Surprise Inference
- Craiu-Duchesne – A generalized estimation equation approach to longitudial conditional logistic regression
- Shahbaba/Neal – Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior
- Neal – Estimating Ratios of Normalizing Constants Using Linked Importance Sampling
- Jain – The GI/ G/ K/ N queue with supplementary variable method

### 2004

- Srivastava – Multivariate Theory For Analyzing High Dimensional Data
- Roberts/Rosenthal – General State Space Markov Chains and MCMC Algorithms
- Bramson/Quastel/Rosenthal-When Can Martingales Avoid Ruin
- Craiu/Lee- Model Selection for the Computing Risks Model with and without masking
- Roberts/Rosenthal/Sousa- Extremal Indices, Geomaetric Ergodicity of Markov Chains, and MCMC
- Neal – Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better
- Craiu-Antithetic Acceleration of the Multiple-Try Metropolis
- Evans/Gutman/Swartz -Relative Surprise Inferences and Computaitons for a Reliability Problem
- Srivastava-Some Tests Criteria for the Covariance Matrix with Fewer Observations Than the Dimension
- Srivastava/Kubokawa – Empirical Bayes Regression Analysis with Many Regressors but Fewer Observations
- Neal – Taking Bigger Metropolis steps by Dragging Fast Variables
- 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
- Evans/Moshonov – Checking for Prior-Data Conflict

### 2003

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

### 2002

- Evans/Zou – On the Robustness of relative suprise inferences to the choice of prior
- Duchesne/Rosenthal – Stochastic Justification of Some Simple Reliability Models*
- Rosenthal – Quantitative convergence rates of Markov chains: A simple account
- Feuerverger/Rosenthal – Achieving Limiting Distributions for Markov Chains Using Back Buttons
- Craiu/Meng – Multi-process Parallel Antithetic Coupling For Backward and Forward Markov Chain Monte Carlo
- Srivastava – Multivariate Analysis With Fewer Observations than the Dimension
- Chen/Hoppe/Iyengar/Brent – A hybrid logistic model fo case-control studies

### 2001

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

### 2000

- Roberts/Rosenthal – Small and Pseudo-Small Sets for markov Chains
- Jain/Rao – State-Dependent Rates In A Finite-Capacity Double-Ended Queue With an Application To Inventory Problem.
- Jain/Neal -A Split-Merge Markov Chain Monte Carlo Procedure For the Dirichlet process Mixture Model
- Roberts/Rosenthal – A note on geometric ergodicity and floating-point roundoff error
- Neal – Slice Sampling
- Alkhamisi/Fraser -On higher order likelihood analysis o the one-way random effects
- Lu/Rosenthal/Shaffer – Crossword puzzles: Experiments with meta-search in propositional reasoning
- Gordon/Rosenthal – Capitalism’s Growth Imperative
- Borodin/Roberts/Rosenthal/Tsaparas – Finding Authorities and Hubs From Link Structures on the World Wide Web
- Srivastava – Nested Growth Curve Models
- Yuen – Generalization of Discrete-tiem Geometric Bounds to Convergence Rate of Markov Process on R
- Glimm/Srivastava – Multivariate Tests of normal mean vectors with restricted Alternatives
- Kollo/Srivastava -A new class of skewed multivariate distributions

### 1999

- Hirotsu/Srivastava – Simultaneous Confidence Intervals Based on One-sided max t Test
- Rosenthal – Parallel computing and Monte Carlo algorithms
- Rosenthal A review of asymptotic convergence for general state space Markov chains
- Roberts/Rosenthal – The Polar Slice Sampler
- Roberts/Rosenthal – Recent progress on computable bounds and the simple slice sampler
- Roberts/Rosenthal – Bayesian models with infinite heirarchies
- Srivastava – Singular Wishart and Multivariate Beta Distributions
- Srivastava/Solanky – Predicitng Multivaritate Response in Linear Regression Model
- Jain/Rao – Computational procededure for the stead-state analysis of a finite-capacity-bulk service doule-ended queueing system
- Neal – Circulary-Coupled Markov Chain Sampling
- Israel/Rosenthal/Wei – Finding generators for Markov chains via empirical transition matrices

### 1998

- Feuerverger/Robinson/Wong – On the Second Order Relative Accuracy of Certain Bootstrap and Saddlepoint Approximation Procedures
- Evans/Swartz – Higher Order envelope Random Variate Generators
- Escobar/West – Computing Bayesian Nonparametiric Hierarchical Models
- Fujikoshi/Seo -Asymptotic Expansion For The Joint Distribution of Correlated Hotelling’s T Statistics Under Normality
- Neal/Annealed Importance Sampling
- Srivastava/von Rosen – Growth Curve Models
- Srivastava-Aoshima – Classification With A Preassigned Error Rate When Two Covariance Matrices are Equal
- Gibbs -Bounding Convergence Time fo the Gibbs Sampler in Bayesian Image Restoration
- Petrone/Roberts – A note on convergence rates of Gibbs sampling for nonparametric mixtures
- Murdoch/Rosenthal – Efficient Use of Exact Samples
- Osborne/Rosenthal/Tanner – Meeetings with costly participation
- Murdoch/Rosenthal – An extension of Fill’s exact sampling algorithm to non-monotone chains
- Jain/Reiss – Busy Periods and Busy Cycles in Bulk-arrival Queueing Systems
- Pemantle/Rosenthal – Moment cnditions for a sequence with negative drift to be uniformaly bounded in L
- Neal – Markov Chain Sampling Methods for Dirichlet Process Mixture Models
- Srivastava/Kubokawa – Improved Nonnegative Estimation of Multivariate Components of Varieance
- Srivastava/Kubokawa – Improved Nonnegative Estimation of Multivariate Components of Varieance
- Roberts/Rosenthal – Sufficient Markov Chain

### 1997

- Pavlenko – Asymptotic behavior of the probabilities misclassification for discriminant functions with weighting
- Neal – Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
- Roberts/Rosenthal – Two convergence properties of hybrid samples
- Efron/Tibshirani – The Problem of Regions
- Robersts/Rosenthal – Markov chain Monte Carlo-Some practical implications of theoretical results
- Srivastava – Resampling Methods for Imputing Missing Observations in Regression Models
- Srivastava – Resampling Methods for Imputing Missing Observations
- Rosenthal/Schwartz – Gambling Systems and Multiplication-Invariant Measures
- Zarepour/Knight – Bootstrapping point processes with some applications
- Andrews-Austin-Quigley – Measuring Warehouse Performance
- Roberts/Rosenthal – On Convergebce rates of Gibbs samplers foruniform distribution
- Roberts/Rosenthal – Convergence of slice sampler Markov chains
- Nagao/rivastava – Fixed Width Confidence Region for The Mean of A Multivaritate Normal Distribution
- Kubokawa/Srivastava – Robust Improvements in Estimation of Mean and Covariance Matrices in Elliptically Contoured Distribution
- Fujikoshi/Seo – Asymptotic Approximations for EPMC’s of the Linear and the Quadratic Disciminant Functions When the Sample Sizes and the Dimension and Large
- Knight – Asymptotics for L Regression estimators under general conditions
- Yuen – Applications of Cheeger’s Constant to the Convergence Rate of Markov Chains on R
- Srivastava -Generalized Multivariate Analysis of Variance Models
- Evans/Swartz – An Algorithm fof the Approximation of Integrals with Exact Error bounds
- Oyet/Wiens – Robust Designs for Wavelet Approximations
- Tibshirani/Knight – The covariance inflation criterion for adaptive model selection
- Neal – Markov Chain Monte Carlo Methods Based on ‘Slicing’ the Density Function
- Seo/Srivastava – Testing Equality of Means and Simultaneus Confidence Intervals in Repeated Measures with Missing Data
- Cowles/Roberts/Rosenthal – Possible biases induced by MCMC convergence diagnostics

### 1996

- Roberts-Rosenthal – Quantitative bonds for convergence rates of continuous time Markov process
- Tibshirani – Bias, variance and prediciton error for classification rules
- Hastie-Ikeda-Tibshirani – Computer-aided diagnosis of mammographic masses
- Cowles-Rosenthal – A simulation approach to convergence rates for Markov chain Monte Carlo allgorithms
- Redelmeier/Tibshirani – Cellular telephones and automobile collisions: some variations on matched case-control analysis
- Evans-Swartz – Ramdom Variable Generation Using Concavity Properties of Transformed Densities
- Neal-Dayan – Factor analysis using Delta-Rule Wake-Sleep learning
- Jain – Autoregressive progress and its applications to Queueing Model
- Roberts-Rosenthal – Geometric ergodicity and Hybrid Markov Chains
- Tibshirani -Who is the fastest man in the world
- Hastie-Tibshirani – Classified by Pairwise Coupling
- Fraser-Reid-Wu – A simple general formula for tail probabilities for frequentist and Bayesian inference

### 1995

- Srivastava – Robustness of Control Procedures For Integrated Moving Average Provess of Order One
- Willmot/Lin – Bounds On The Tails of Convolutions Of Compound Distributions
- Srivastava/Wu – Evaluation of Optimum Weights and Average Run Lengths in EWMA Control Schemes
- Zarepour/Knight – Bootstrapping unstable first order autoregressive processes with errors in the domain of attraction of stable law
- Reid – Higher order asymptotics and likelihood: a review
- Reid – Statistics in the twenty-first century: Asymptotic theory and the foundations of statistics
- Roberts/Rosenthal – Optimal scaling of discrete approximations to Langevin diffusions
- Neal – Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation
- Rosenthal – Faithful couplings of Markov chains now equals forever
- Shivastava – Reduced Rank Discrinimation
- Evans-Swartz – Bayesian Integration Using Multivariate Student Important Sampling
- Srivastava – CUSUM procedure for Monitoring Variability
- Willmot-Lin- Bounds On The Tails of Convolutions of Compound Distributions
- Jain – a Comparison of sochastically Ordered Queues
- Roberts-Rosenthal-Schwartz – Convergence properties of perturbed Markov chains
- Tibshirani/Knight – Model search and inference by bootstrap “bumping”
- Kubokawa/Srivastava – Double Shrinkage Estimators of Ratio of Variances

### 1994

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

### 1993

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

### 1992

- Evans – The surprise distribution and some uses in statistical inference
- 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
- Srivastava/Wu – On-line Quality Control Procedures based on Random Walk Model and Integrated Moving Average Model of Order (0,1,1)
- Guttman/Pena – A Bayesian Look At Diagnostics In The Univariate Linear Model
- Wang – Smoothing Splines for Non-parametric Regression Percentiles
- Pena/Guttman – Comparing Probabilistic Methods for Outlier Detection
- Reiser/Guttman/Lin/Guess/Usher – Bayesian Inference for Masked System Life Time Data
- 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
- Hastie/Buja/Tibshirani – Penalized Discriminant Analysis
- Hastie/Tibshirani – Handwritten Digit Recognition via Deformable Prototypes
- Guttman/Pena – A Bayesian Look At Diagnostics In The Univariate Linear Model
- Lin/Guttman – Handling spuriosity in the Kalman filter
- Mo/Wang – Asymptotic Normality for Estimators of Eigenvectors
- Srivastava/Chow – A Comparison of Some OMNIBUS CUSUM and OMNIBUS EWMA Statistical Process Control Procedures

### 1991

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

### 1990

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

### 1989

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

### 1988

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

### 1987

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

### 1986

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

### 1985

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

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

### 1983

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

### 1982

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

### 1981

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

### 1980

- Srivastava/Carter – Asymptotic Distribution of Latent Roots and Applications
- Srivastava – Multivariate Data with Missing Observations
- Srivastava – On Tests for Detecting Change in the Multivariate Mean
- 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
- Waugh – Application of the Galton-Watson Process to the Kin Number Problem