Factor screening for simulation with multiple responses: Sequential bifurcation

The goal of factor screening is to find the really important inputs (factors) among the many inputs that may be changed in a realistic simulation experiment. A specific method is sequential bifurcation (SB), which is a sequential method that changes groups of inputs simultaneously. SB is most efficient and effective if the following assumptions are satisfied: (i) second-order polynomials are adequate approximations of the input/output functions implied by the simulation model; (ii) the signs of all first-order effects are known; and (iii) if two inputs have no important first-order effects, then they have no important second-order effects either (heredity property). This paper examines SB for random simulation with multiple responses (outputs), called multi-response SB (MSB). This MSB selects groups of inputs such that—within a group—all inputs have the same sign for a specific type of output, so no cancellation of first-order effects occurs. To obtain enough replicates (replications) for correctly classifying a group effect or an individual effect as being important or unimportant, MSB applies Wald’s sequential probability ratio test (SPRT). The initial number of replicates in this SPRT is also selected efficiently by MSB. Moreover, MSB includes a procedure to validate the three assumptions of MSB. The paper evaluates the performance of MSB through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; the results are very promising.     

The analysis of menstrual bleeding patterns: A review

Patterns of vaginal bleeding are an important factor in the acceptability of contraceptive methods. The analysis of data obtained from daily menstrual diary records is a major methodological problem to which no satisfactory solution exists. This review describes approaches to the analysis of bleeding patterns among contracepting and non-contracepting women and reviews the difficulties involved. The reference period method, introduced to avoid the arbitrary rules and definitions required for an analysis based on the concept of a menstrual cycle, is discussed and its limitations presented. The review draws on reports of meetings convened by the World Health Organization and the University of Exeter Family Planning Unit to discuss issues in the analysis. Previously unpublished methods are summarized and areas of controversy and topics for further research are identified.     

Computer-Assisted Statistics Education at Delft University of Technology

Abstract

At Delft University of Technology many students experience difficulties in mastering basic concepts of probability and statistics. In the past few years the lectures have undergone a radical change—the lecture notes now contain modern data analysis techniques, like kernel density estimation, simulation, and bootstrapping. In the TWI-Stat project, a computer-aided instruction course was developed to help students become more familiar with modern statistical analysis. The course presents itself as a dynamic, interactive, personal book. Highly interactive analysis tools are available. The software will be available for MS-Windows.     

Data analysis and decision support

In this paper, we consider developmental lines of computer-assisted decision support (with consideration of knowledge-based approaches) for data analysis problems. First, we discuss some situations where it is obviously appropriate to apply computer-assisted decision support in connection with data analysis tasks. Then, a brief historical retrospect is given viewing the development of this area of research and its interfaces to knowledge-based approaches. Against this background we illustrate two prototypes of knowledge-based decision support systems for specific data-analysis problems related to fields of interest of our own. Finally, we indicate possible progress and future activities in this area.     

Forbidden patterns and shift systems

The scope of this paper is two-fold. First, to present to the researchers in combinatorics an interesting implementation of permutations avoiding generalized patterns in the framework of discrete-time dynamical systems. Indeed, the orbits generated by piecewise monotone maps on one-dimensional intervals have forbidden order patterns, i.e., order patterns that do not occur in any orbit. The allowed patterns are then those patterns avoiding the so-called forbidden root patterns and their shifted patterns. The second scope is to study forbidden patterns in shift systems, which are universal models in information theory, dynamical systems and stochastic processes. Due to its simple structure, shift systems are accessible to a more detailed analysis and, at the same time, exhibit all important properties of low-dimensional chaotic dynamical systems (e.g., sensitivity to initial conditions, strong mixing and a dense set of periodic points), allowing to export the results to other dynamical systems via order-isomorphisms.     

Users,Programmers, and Statistical Software

Abstract

Statistical software provides essential support for statisticians and others who are analyzing data or doing research on new statistical techniques. Those supported typically regard themselves as “users” of the software, but as soon as they need to express their own ideas computationally, they in fact become “programmers.” Nothing is more important for the success of statistical software than enabling this transition from user to programmer, and on to gradually more ambitious software design. What does the user need? How can the design of statistical software help? This article presents a number of suggestions based on past experience and current research. The evolution of the S system reflects some of these opinions. Work on the Omegahat software provides a promising direction for future systems that reflect similar motivations.     

Developing an efficient knowledge discovering model for mining fuzzy multi-level sequential patterns in sequence databases

Sequential pattern mining from sequence databases has been recognized as an important data mining problem with various applications. Items in a sequence database can be organized into a concept hierarchy according to taxonomy. Based on the hierarchy, sequential patterns can be found not only at the leaf nodes (individual items) of the hierarchy, but also at higher levels of the hierarchy; this is called multiple-level sequential pattern mining. In previous research, taxonomies based on crisp relationships between any two disjointed levels, however, cannot handle the uncertainties and fuzziness in real life. For example, Tomatoes could be classified into the Fruit category, but could be also regarded as the Vegetable category. To deal with the fuzzy nature of taxonomy, Chen and Huang developed a novel knowledge discovering model to mine fuzzy multi-level sequential patterns, where the relationships from one level to another can be represented by a value between 0 and 1. In their work, a generalized sequential patterns (GSP)-like algorithm was developed to find fuzzy multi-level sequential patterns. This algorithm, however, faces a difficult problem since the mining process may have to generate and examine a huge set of combinatorial subsequences and requires multiple scans of the database. In this paper, we propose a new efficient algorithm to mine this type of pattern based on the divide-and-conquer strategy. In addition, another efficient algorithm is developed to discover fuzzy cross-level sequential patterns. Since the proposed algorithm greatly reduces the candidate subsequence generation efforts, the performance is improved significantly. Experiments show that the proposed algorithm is much more efficient and scalable than the previous one. In mining real-life databases, our works enhance the model's practicability and could promote more applications in business.     

Extracting approximate patterns

In this paper, we define a family of patterns with don't cares occurring in a text. We call them primitive patterns. The set of primitive patterns forms a basis for all the maximal patterns occurring in the text. The number of primitive patterns is smaller than other known basis.

We present an incremental algorithm that computes the primitive patterns occurring at least q times in a text of length n, given the N primitive patterns occurring at least q−1 times, in time O(|Σ|Nn²logn), where Σ is the alphabet. In the particular case where q=2, the complexity in time is only O(|Σ|n²logn). We also give an algorithm that decides if a given pattern is primitive in a given text. These algorithms are generalized, taking many sequences in input. Finally, we give a solution for reducing the number of patterns of interest by using scoring techniques, as we show that the number of primitive patterns is exponential.     

Vaginal bleeding patterns — the problem and an example data set

Data concerning the vaginal bleeding patterns of women using different forms of fertility regulation are presented. The data were collected in the form of diaries which were completed by the women themselves by recording the presence or absence of vaginal bleeding on a daily basis. The object of the paper is to invite suggestions for suitable methods of presentation and analysis of such data so that the results of studies on methods of fertility regulation can be better summarized and interpreted.     

Extensible Statistical Software: On a Voyage to Oberon

Abstract

Recent changes in software technology have opened new possibilities for statistical computing. Conditions for creating efficient and reliable extensible systems have been largely improved by programming languages and systems that provide dynamic loading and type-safety across module boundaries, even at run time. We introduce Voyager, an extensible data analysis system based on Oberon, which tries to exploit some of these possibilities.     

Moon patterns,sun patterns,and wave breaking in rotating granular mixtures

Granular materials, such as powders and sand, tend to segregate due to differences in particle properties. When a cylindrical drum is partially filled with particles of different sizes and rotated about its axis, this leads to radial segregation patterns in which the smaller particles concentrate in a radial core near the axis, and the larger particles near the outside walls of the drum. Under certain conditions, undulations in the radial core of smaller particles grow into radial stripes that extend toward the outer walls of the drum in a manner somewhat reminiscent of viscous fingering. The patterns are strongly dependent on the fill level and rotation speed of the drum. These observations can be explained by two spatially disjoint mechanisms: (1) a wave‐breaking mechanism that promotes the growth of the stripes and (2) a filtering mechanism that limits the growth of stripes. © 2005 Wiley Periodicals, Inc. Complexity 10: 79–86, 2005     

Toward a Common Framework for Statistical Analysis and Development

We develop a general ontology of statistical methods and use it to propose a common framework for statistical analysis and software development built on and within the R language, including R's numerous existing packages. This framework offers a simple unified structure and syntax that can encompass a large fraction of existing statistical procedures. We conjecture that it can be used to encompass and present simply a vast majority of existing statistical methods, without requiring changes in existing approaches, and regardless of the theory of inference on which they are based, notation with which they were developed, and programming syntax with which they have been implemented. This development enabled us, and should enable others, to design statistical software with a single, simple, and unified user interface that helps overcome the conflicting notation, syntax, jargon, and statistical methods existing across the methods subfields of numerous academic disciplines. The approach also enables one to build a graphical user interface that automatically includes any method encompassed within the framework. We hope that the result of this line of research will greatly reduce the time from the creation of a new statistical innovation to its widespread use by applied researchers whether or not they use or program in R.     

A Grammar for Reproducible and Painless Extract-Transform-Load Operations on Medium Data

Many interesting datasets available on the Internet are of a medium size—too big to fit into a personal computer’s memory, but not so large that they would not fit comfortably on its hard disk. In the coming years, datasets of this magnitude will inform vital research in a wide array of application domains. However, due to a variety of constraints they are cumbersome to ingest, wrangle, analyze, and share in a reproducible fashion. These obstructions hamper thorough peer-review and thus disrupt the forward progress of science. We propose a predictable and pipeable framework for R (the state-of-the-art statistical computing environment) that leverages SQL (the venerable database architecture and query language) to make reproducible research on medium data a painless reality. Supplementary material for this article is available online.     

Information patterns and Nash equilibria in extensive games: 1

In this paper we explore the relation between information patterns and Nash Equilibria in extensive games. By information we mean what players know about moves made by others, as well as by chance. For the most part we confine ourselves to pure strategies. But in Section 2 behavioral strategies are also examined. It turns out that they can be modeled as pure strategies of an appropriately enlarged game. Our results, applied to the enlarged game, can then be reinterpreted in terms of the behavioral strategies of the original game.     

An algorithmic study of the Maximum Flow problem: A comparative statistical analysis

In this paper, we show the results of an experimental study about the most important algorithms proposed to solve the Maximum Flow problem. The appropriate statistical analysis not only allows us to justify comparisons between the different procedures but also to obtain classifications of their practical efficiency. Furthermore, an empirical experiment allows us to identify the influence of several parameters that are not included in a theoretical study.     

A framework for statistical software development, maintenance, and publishing within an open-access business model

There are several fundamental problems with statistical software development in the academic community. In addition, the development and dissemination of academic software will become increasingly difficult due to a variety of reasons. To solve these problems, a new framework for statistical software development, maintenance, and publishing is proposed: it is based on the paradigm that academic and commercial software should be both cost-effectively created, maintained and published with Marketing Principles in mind. The framework has been seamlessly integrated into a highly successful website () that operates as a provider of free web-based statistical software. Finally it is explained how the R framework provides a platform for the development of a true compendium publishing system.     

Breaks, cuts, and patterns

We generalize the concept of a break by considering pairs of arbitrary rounds. We show that a set of home-away patterns minimizing the number of generalized breaks cannot be found in polynomial time, unless P=NP. When all teams have the same break set, the decision version becomes easy; optimizing remains NP-hard.