# Statistics Seminars

### 2004/2005

Note: the program is not final and is subject to possible changes

### First Term

 2, November Ori Davidov,   Haifa University When is the mean self-consistent ? 30, November Anat Sakov, Tel Aviv University Mice Behavior and Laboratories,  Statistical Challenges and Proposed Solutions 21, December David Steinberg, Tel Aviv University Identifying critical parameters in simulations: a case study of a nuclear waste repository 28, December Saharon Rosset, IBM Watson A Method for Inferring Label Sampling Mechanisms in Semi-Supervised  Learning 4, January Eitan Bachmat, Ben Gurion University Airplane boarding, disk I/O scheduling, patience sorting, surface growth and space-time geometry

### Second Term

 22, February Roelof Helmers, CWI, Amsterdam, The Netherlands The Empirical Edgeworth expansion for a studentized trimmed mean 14, March* Tsachy Weismann, Stanford University 15, March Peter McCullagh, University of chicago 22, March Malka Gorfine, Bar Ilan University Survival analysis with general semiparametric shared frailty model - prospective and retrospective designs. 27, March* Ibragimov On estimation of analytic functions 29, March Amir Herman, Tel Aviv University 24, May Nicole A. Lazar,  University of Georgia The Use of Resampling and Visualization for the Comparison of Changepoint Location in Two Independent Curves

### Summer Term

 27, June* Jay H. Beder,    University of Wisconsin - Milwaukee Box-Hunter resolution in arbitrary fractional factorial designs 11, July* Abraham Wyner, Department of Statistics The Wharton School, University of Pennsylvania

Seminars are held on Tuesdays, 10.30 am, Schreiber Building, 309 (see the TAU map ).     is served before.

* Seminar held at other  time and place.

The seminar organizer is Daniel Yekutieli. To join the seminar mailing list and get updated information about current/forthcoming seminars

and for other inquiries call (03)-6409612 or email yekutiel@post.tau.ac.il

Details of previous seminars:

### ABSTRACTS

• Ori Davidov,   Haifa University

When is the mean self-consistent ?

We study the conditions under which the sample mean is self consistent, and therefore an optimal predictor, for an arbitrary observation in the sample.

• Anat Sakov,   Tel Aviv University

Mice Behavior and Laboratories,  Statistical Challenges and Proposed Solutions

In the field of behavior genetics, behavior patterns of mice genotypes (strains) are characterized via different measures (end-points), in order to associate them with particular genes. Genotypes are usually compared within a single laboratory, and questions regarding the replicability of results from one laboratory to the other eventually arise. We propose to view this problem using the mixed-effects model. The replicability problem is relevant whenever observations and conclusions are extended beyond a single laboratory (and not only in behavior genetics).

Our approach is presented in the context of a mouse loco-motor behavior. Mouse locations in an arena are recorded, pre-processed, and than end-points are computed.  The process is executed in different laboratories. The differences between genotypes are then assessed using the mixed model.

Time permitting, we will discuss our ongoing research: many end-points are measured on each mouse, and dimension reduction becomes of interest. However, due to the complexity of the data, usual  principal component analysis is not valid. We present the problem, our strategy and preliminary results.

This is a joint work with Yoav Benjamini, Neri Kafkafi, Greg Elmer, Itay Hen, Ilan Golani and his students.

### ·        David M. Steinberg, Tel Aviv University

Identifying critical parameters in simulations: a case study of a nuclear waste repository

### ·        Saharon Rosset, IBM Watson

A Method for Inferring Label Sampling Mechanisms in Semi-Supervised  Learning

### ·        Eitan Bachmat, department of computer science, BGU

Airplane boarding, disk I/O scheduling, patience sorting, surface growth and space-time geometry

### ·        Roelof Helmers, CWI, Amsterdam, The Netherlands

The Empirical Edgeworth expansion for a studentized trimmed mean


We establish the validity of the empirical Edgeworth expansion(EE) for a studentized trimmed mean ,under the sole condition that the underlying
distribution function of the observations satisfies a local smoothness condition near the two quantiles where the trimming occurs.
A simple explicit formula for the $N^{-1/2}$ term ,correcting for skewness and bias($N$ being the sample size) of the EE will be given.
In particular our result supplements previous work by Hall and Padmanabhan(1992) and Putter and van Zwet (1998).
The proof is based on a U-statistic type approximation and also uses a version of Bahadur's (1966) representation for sample quantiles.

This is joint work with Nadezhda Gribkova(St.Petersburg).


### ·        Ibragimov

On estimation of analytic functions

No abstract provided

### ·        Amir Herman, Tel Aviv University

Analyzing Leukemia Survival Data Focusing on  non-Remissing and Remissing patients Amir Herman

Analyzing Survival Data in oncological diseases (e.g. Leukemia) is different from analyzing survival data for non oncologic diseases. In the classic survival data analysis the event is defined as death of a patient. However in leukemias and some other cancers we often use the ‘event free survival’ to describe the desirable end point of the treatment. In this case an event is considered to be either relapses of the disease or the death of the patient.

That endpoint entails a problem in it. That is: not all the patients are disease free at the point of entry to the study. In fact, inducing remission of the disease is also one of the desirable endpoints of a treatment.

In our work we compare two methods to analyze such data. One method is to include the non-remissing patients as events at time zero, since the disease in these patients has never been remitted. The second method is to split the analysis and to analyze the patients from diagnosis to remission and from remission to the familiar endpoint of “relapse or death”.

We conducted a simulation to examine the errors and the influential parameters on the bias of the parameter estimates using the Cox proportional hazards model. The bias of the estimator from the split method was influenced by the sample size and time of follow-up. The main parameters that influenced the parameter estimate of the method with events at time zero, were the relative risk at time 0 and the proportional size of the non-remissing patients group.

We then analyzed pediatric AML data using the split analysis approach and the whole analysis approach. Another advantage of this approach was the ability to include variables that exist only at the time of remission. Such a variable is the remission index defined as:

{log (platelets at time of remission)/log(platelets at time of diagnosis)}* {log (Hemoglobin at time of remission)/log(Hemoglobin at time of diagnosis)}. This index was found to be an important prognostic factor for remaining in remission.

### ·        Jay H. Beder,  Dept. of Mathematical Sciences,    University of Wisconsin – Milwaukee

Box-Hunter resolution in arbitrary fractional factorial designs

In 1961 Box and Hunter defined the resolution of a regular fractional factorial design as a measure of the amount of aliasing in the fraction. They indicated that the maximum resolution is equal to the minimum length of a defining word.

Since then, various approaches have been offered to generalize the concept of resolution to arbitrary (possibly mixed-level) fractions.
These have generally been based on estimability and on the assumption that high-order interactions are absent, rather than on the alias structure of the fraction. In this talk we will formulate a generalization of Box-Hunter resolution based on an idea that may be traced back to Rao (1947). Using it, we show that in an arbitrary fraction of maximum strength t and maximum resolution R, we have R = t+1. This generalizes the wordlength criterion.

·        Abraham Wyner, Department of Statistics The Wharton School, University of Pennsylvania