Computer-intensive Statistical Methods Research Articles

Developments in statistical analysis in quantitative genetics

A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and computer-intensive statistical methods. The latter were revolutionized by the bootstrap and by Markov chain Monte Carlo (McMC). In this overview a number of specific areas are chosen to illustrate the enormous flexibility that McMC has provided for fitting models and exploring features of data that were previously inaccessible. The selected areas are inferences of the trajectories over time of genetic means and variances, models for the analysis of categorical and count data, the statistical genetics of a model postulating that environmental variance is partly under genetic control, and a short discussion of models that incorporate massive genetic marker information. We provide an overview of the application of McMC to study model fit, and finally, a discussion is presented on the development of efficient McMC updating schemes for non-standard models.

Approach to accuracy assessment of the glass-electrode potentiometric determination of acid-base properties

Thermodynamic data are suitable subject for investigating strategies and concepts for the evaluation of complete measurement uncertainty budgets in situations where the measurand cannot be expressed in a mathematical formula. Some suitable approaches are the various forms of Monte Carlo simulations in combination with computer-intensive statistical methods that are directed to an evaluation of empirical distribution curves for the uncertainty budget. Basis of the analysis is a cause-and-effect diagram. Some experience is available with cause-and-effect analysis of thermodynamic data derived from spectrophotometric data. Another important technique for the evaluation of thermodynamic data is glass-electrode potentiometry. On basis of a newly derived cause-and-effect diagram, a complete measurement uncertainty budget for the determination of the acidity constants of phosphoric acid by glass-electrode potentiometry is derived. A combination of Monte Carlo and bootstrap methods is applied in conjunction with the commercially available code SUPERQUAD. The results suggest that glass-electrode potentiometry may achieve a high within-laboratory precision because major uncertainty contributions become evident via interlaboratory comparisons. This finding is further underscored by analysing available literature data.

PsN-Toolkit—A collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM

PsN-Toolkit is a collection of statistical tools for pharmacometric data analysis using the non-linear mixed effect modeling software NONMEM. The toolkit is object oriented and written in the programming language Perl using the programming library Perl-speaks-NONMEM (PsN). Five methods: the Bootstrap, the Jackknife, Log-likelihood Profiling, Case-deletion Diagnostics and Stepwise Covariate Model building are included as separate classes and may be used in user-written Perl scripts or through stand-alone command line applications. The tools are designed with the ability to cooperate and with an emphasis on common structures for workflow and result handling. Parallel execution of independent tool sections is supported on shared memory multiprocessor (SMP) computers, Mosix/openMosix clusters and distributed computing environments following the NorduGrid standard. In conclusion, PsN-Toolkit makes it easier to use the Bootstrap, the Jackknife, Log-likelihood Profiling, Case-deletion Diagnostics and Stepwise Covariate Model building in pharmacometric data analysis.

Spectroscopy, chemometrics and metrology—three aspects of lanthanide chemistry

Using spectroscopic data of the Nd(III)–nicotinic acid N-oxide (nic NO) system in methanol as example data, the requirements of the ISO Guide to the Expression of Uncertainty to the evaluation of spectroscopic data are demonstrated. The cause-and-effect diagram approach suggested by EURACHEM is shown and applied to the evaluation of single-component spectra and thermodynamic data of the Nd(III)-nic NO system using computer-intensive statistical methods like the threshold bootstrap in combination with target factor analysis (FA).

Robustness comparison of bow-tie and dipole antennas

The paper describes the application of a computer intensive methodology to evaluate antenna susceptibility to random physical damage for a dipole and a bow-tie antenna. For each randomly generated damage pattern antenna parameters are simulated and a statistical comparison is performed. Computer intensive statistical methods like the Bootstrap, used here for postprocessing, make fewer initial assumptions than classical statistical methods when modelling a problem, and are robust against small deflections from these assumptions. The bias-corrected and accelerated (BCa) Bootstrap method is used for confidence interval estimates. The dipole antenna is found to be more robust to numbers of small (∼100th of a wavelength) circular holes penetrating the structure than the bow-tie antenna.

Statistical analysis of the impact of spectral correlation on observed formation constants from UV-visible spectroscopic measurements.

Information retrieved from UV-visible spectroscopic data by application of a self-modelling factor analysis algorithm showed apparently systematically shifted thermodynamic properties for the same chemical system as a function of spectral slit widths. This empirical observation triggered a systematic investigation into the likely effects of residual and spectral correlation on the numerical results from quantitative spectroscopic investigations. If slit width was a nuisance factor it would reduce the comparability of information evaluated from spectroscopic data. The influence of spectral slit width was investigated by simulation, i.e. by generating and evaluating synthetic spectra with known properties. The simulations showed that increasing spectral correlation may introduce bias into factor analysis evaluations. By evaluation of the complete measurement uncertainty budget using threshold bootstrap target factor (TB CAT) analysis, the apparent shifts are insignificant relative to the total width of the quantity's measurement uncertainty. Increasing the slit widths causes some systematic effects, for example broadening of the registered spectral bands and reduction of spectral noise, because of higher light intensity passing to the detector. Hence, the observed systematic shifts in mean values might be caused by some latent correlation. As a general conclusion, slit width does not affect bias. However, the simulations show that spectral correlation and residual correlation may cause bias. Residual correlation can be taken into account by computer-intensive statistical methods, for example moving block or threshold bootstrap analysis. Spectral correlation is a property of the chemical system under study and cannot be manipulated. As a major result, evidence is given showing that stronger spectral correlation ( r<-0.7) causes non-negligible bias in the evaluated thermodynamic information from such a system.

Measurement uncertainty of thermodynamic data.

Thermodynamic quantities of chemical reactions are commonly derived from experimental data obtained by chemical analysis. The accuracy of the evaluated thermodynamic quantities is limited by the measurement uncertainty of the analytical techniques applied. Straightforward transfer of metrological rules established for determination of single analytes to the more complex process of evaluating values of thermodynamic quantities is not possible. Computer-intensive statistical methods and Monte Carlo techniques are shown to enable integration of existing metrological concepts. An initial stage of the integration of both concepts is presented, taking solubility data for Am(III) in carbonate media as an illustrative example. A cause and effect diagram is created as a means of identification of sources of uncertainty. The uncertainties are used in a resampling-based Monte Carlo study to produce a probability distribution of the value of a quantity.

Computer-intensive methods in statistical analysis

As far back as the late 1970s, the impact of affordable, high-speed computers on the theory and practice of modern statistics was recognized by Efron (1979, 1982). As a result, the bootstrap and other computer-intensive statistical methods (such as subsampling and the jackknife) have been developed extensively since that time and now constitute very powerful (and intuitive) tools to do statistics with. This article provides a readable, self-contained introduction to the bootstrap and jackknife methodology for statistical inference; in particular, the focus is on the derivation of confidence intervals in general situations. A guide to the available bibliography on bootstrap methods is also offered.

The Bootsrap Useful for Accounting Researchers?

Over the past 15 years, there has been tremendous interest in the area of computer-intensive statistical methods. One specific technique whose use has become increasingly widespread is the “bootstrap”. Using the computational power of computers, it is possible to obtain estimates of standard error and bias and to construct confidence intervals for estimators without having to make assumptions about the sample distribution of the estimator. To date, little use of the bootstrap has been made in accounting research primarily because few accounting researchers were aware of its existence. This article provides an intuitive overview of the bootstrap, demonstrates the technique using three examples and provides information regarding the software which is available to implement bootstrapping techniques.

Research Methodology

With the availability of powerful, inexpensive computer hardware and software, computer-intensive statistical methods are becoming more commonly applied in the social sciences. These methods frequently offer a number of advantages over traditional parametric procedures. This article briefly reviews four computer-intensive methods (permutation/randomization tests, bootstrapping, the jack knife, and cross-validation), discusses some of the strengths and weaknesses of these approaches, provides example applications, and discusses five commercially available software packages that may be used to implement these methods.

Empirical methods for artificial intelligence

Empirical research exploratory data analysis basic issues in experiment design hypothesis testing and estimation computer-intensive statistical methods performance assessment explaining performance - interactions and dependencies modelling tactics for generalization.

A New Way of Teaching Statistics

Teaching statistics through computer-assisted simulations eliminates the constraints and challenges associated with teaching the course using mathematics. It also provides students with a practical means for solving real-life problems and a solid conceptual grasp of the problem-solving nature of the discipline. A text that deemphasizes mathematics and introduces simulation as a means of understanding concepts, along with software designed for computer-intensive statistical methods and a workbook of journal article selections provide the foundation materials for such a study of statistics. A special course guide also was developed to provide a clear introduction to the software for naive users, show how the software and the text are related, and connect the simulation techniques to standard statistical tests. Altogether these materials not only provide a positive experience for students studying statistics, but they allow them to study the subject independently and at a distance.

Computer Intensive Statistical Methods: Validation, Model Selection, and Bootstrap

This book focuses on computer intensive statistical methods, such as validation, model selection, and bootstrap, that help overcome obstacles that could not be previously solved by methods such as regression and time series modelling in the areas of economics, meteorology, and transportation.

Computer Intensive Statistical Methods: Validation, Model Selection, and Bootstrap

Preface. Prelude. Computer Intensive Philosophy. Cross Validation. Validation of Time Series Problems. Statistical Bootstrap. Further Bootstrap Results. Computer Intensive Applications. References. Index.

Racetrack wagering and the “uninformed” bettor: A study of market efficiency

The analysis examines the wagering behavior of the “uninformed” bettor. Using data from a second tier racetrack, the high correlation between subjective and objective probabilities suggests an efficient win pool. However, the results fail to demonstrate the public 's tendency to underbet favorites and overbet longshots. Employing computer intensive statistical methods, a second part of the analysis suggests that mispricing does exist in the place and show pooh, although the size of these inefficiencies may be of small economic consequence. Taken as a whole, the results of a small, start-up track mirror those found in earlier studies of major racetracks and support an earlier conjecture that the fast, repetitive feedback in horserace wagering facilitates learning.

Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap

Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap

Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap.

Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap.

Statistical modelling of regional GIS data: an overview

Abstract Rapid advances in computing technology have facilitated the storage and manipulation of large sets of data in Geographic Information Systems. These data present new opportunities for modelling spatial distribution and change to distribution at a regional scale. The models used should take account of the special nature of GIS data, particularly the differing resolution associated with different variables. Likewise, inferences, either concerning model selection or model predictions, should be made with reference to the sampling units on which the estimates are based. Computer-intensive statistical methods, especially the bootstrap, are suitable for studying the variability of predictions. Presentation of results in map form should be accompanied by estimates of error.

コンピュータを活用する新しい統計学

The role of Behaviormetrics is to develop and apply quantitative methodologies for analizing phenomena concerning human behaviors in various fields. To quantify, it is unavoidable to carry out data analysis, sooner or later in the research process. Data analysis, for example, is indispensable for either building or testing models. Consequently, Behaviormetrics has a lot to do with Statistics, the branch of science concerning data and data analysis. Some standard methodologies for statistical data analysis are in particular useful for behaviormetricians. On the other hand, Statistics is undergoing a dramatic change due to the drastic improve ment of both computing environment and the ability of computer itself. First, as a result, with the easy access of computers, the population involving data analysis has been seeing a great increase, not restricted in the fields of natural sciences and technology, but also in the fields of social sciences and humanity. While this increase obviously reflects the availability of many new and easy-to-use softwares, it is also the mighty resource for developing even better softwares. Secondly, the computational revolution exerts direct influences on statistical theories themselves, which may be summarized by the appearence in Statistics in late 70's and early 80' s of so called computer-intensive methods. Some of these methods had been of mere theoretical interest to statisticians, but are ready to be put into practical use now. Some are, of course, totally innovative and unthinkable even 20 or 30 years ago. Here we introduce to our readers some of these new methods for data analysis and discuss their potentials for practical use in near future. We realize that time is needed for practitioners to be at ease with any of these methods. However, this is generally true for any new idea in science and technology, if we look back at the history. For example, factor analysis and many other multivariate statistical methods, once a world out of our reach, are now employed by many of us, as confidently as we handle the more usual t-test or analysis of variance(although we can' t say there exists no problem in the way of application). In the same way, we have sound reason to believe that the day should not be too long for many of these new methods, as powerful and versatile tools, to be applied widely and efficiently. In the sequel, we introduce some of these computer-intensive methods, discuss why and how they are conceived, and indicate the implications for applications. Our discussion is divided into two parts, with each of considerable length and relatively independent of the other. With ample practical examples, Part I gives an easy introduction to sevaral methods for data analysis which utilize computer intensively. Section1deals briefly with the historicalintimate relationship between computation and Statistics. Section 2 concerns nonlinear regression analysis. Section 3 discusses computer-aided design of experiments. It ends with a brief summary in Section 4. Our second part deals with bootstrap, which may be regarded as the typical example of computer-intensive methods in Statistics. This part covers almost all the research work about bootstrap and related studies untill the present day time. The historical background and aquick introduction to bootstrap are given in Section1and2, respectively. The applications of bootstrap to error estimation, mainly biases and variances, and to construction of confidence intervals, are given detailed treatment in Section3 and 4, respectively. Section 5 treats acomparatively less known application, i. e. regression analysis. We are especially ignorant of non-parametric regression. In carrying out bootstrapping, it is of great importance to find efficient resampling schemes, to save time and money, and this is the topic of Section6. Last section, Section7, is devoted to the discussion of many undergoing researches.

New statistical methods for directional data-I. Bootstrap comparison of mean directions and the fold test in palaeomagnetism

SUMMARY This paper, and a companion paper, present some new computer-intensive statistical methods for handling small-sample problems in directional data analysis in areas where existing methods are not valid. Here, we describe ‘bootstrap’ tests for comparing the the mean directions of two or more samples of 2-D or 3-D unit vectors. A case of particular interest is the so-called fold test in palaeomagnetism, a situation iri which sample sizes tend to be small, and sample dispersions different. Bootstrap methods give the first satisfactory method for handling this problem. Examples are given for both circular and spherical data.