One-step local quasi-likelihood estimation

Local quasi-likelihood estimation is a useful extension of local least squares methods, but its computational cost and algorithmic convergence problems make the procedure less appealing, particularly when it is iteratively used in methods such as the back-fitting algorithm, cross-validation and bootstrapping. A one-step local quasi-likelihood estimator is introduced to overcome the computational drawbacks of the local quasi-likelihood method. We demonstrate that as long as the initial estimators are reasonably good, the one-step estimator has the same asymptotic behaviour as the local quasi-likelihood method. Our simulation shows that the one-step estimator performs at least as well as the local quasi-likelihood method for a wide range of choices of bandwidths. A data-driven bandwidth selector is proposed for the one-step estimator based on the pre-asymptotic substitution method of Fan and Gijbels. It is then demonstrated that the data-driven one-step local quasi-likelihood estimator performs as well as the maximum local quasi-likelihood estimator by using the ideal optimal bandwidth.     

Reliability assessments by repair shops via maintenance data∗$Fr1:$Sup;∗$esup;$fr:$sup;∗$esup;This is the revised version (containing further applicative examples) of the paper published in the proceedings of the Reliability ‘87 Conference, 14–16 April,Birmimgham, U.K., as anticipated during the oral presentation given then.$ef.

Sometimes, the normal maintenance data recorded by repair shops, working around systems as small as cars or as large as factories, can be used to perform reliability evaluations both in terms of distribution of times to first failure and in terms of expected number of failure in a given time interval. Some examples fiom the field show the use of two estimation procedures proposed for parts and systems respectively, in the context of Non-stationary Stochastic Process. Every attempt has been made to make the whole work practice-oriented, as well as in relation to dimensioning and managing the repair shops themselves.     

Tests for time series of counts based on the probability-generating function

We propose testing procedures for the hypothesis that a given set of discrete observations may be formulated as a particular time series of counts with a specific conditional law. The new test statistics incorporate the empirical probability-generating function computed from the observations. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is included as well as real-data examples.     

Estimating age-specific survival rates of tawny owls--recaptures versus recoveries

We compared estimates of annual survival rates of tawny owls ( Strix aluco ) ringed in southern Finland from several different sampling methods: recoveries of birds ringed as young; recaptures of birds ringed as young; recoveries of birds ringed as adults as well as young; combined recoveries and recaptures of birds ringed as young, and combined recoveries and recaptures of birds ringed as adults and young. From 1979 to 1998, 18 040 young owls were ringed, of which 983 were recaptured as breeders in subsequent years during this period, and 1764 were recovered dead at various locations. In addition, 1751 owls were ringed as adults, of which 612 were later recaptured and 199 were recovered dead. First-year survival rates estimated using only recoveries of birds ringed as young averaged 48%, while apparent survival rates estimated using only recaptures from birds ringed as young averaged 10-13%. Use of combined recapture-recovery models, or supplementary information from recoveries of birds ringed as adults, produced survival estimates of 30-37%. Survival estimates from young-recoveries-only models were biased high, because of violation of the assumption of constant recovery rates with age: birds dying in their first-year were one-third less likely to be found and reported than older birds. In contrast, recaptures-only models confounded emigration with mortality. Despite these differences in mean values, annual fluctuations in estimated first-year survival rates were similar with all models. Estimates of adult survival rates were similar with all models, while those for second-year birds were similar for all models except recaptures-only. These results highlight the potential biases associated with analysing either recaptures or recoveries alone of birds ringed as young, and the benefits of using combined data.     

A genetic algorithm tutorial

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.     

On the Use of Semi-folding in Regular Blocked Two-level Factorial Designs

In this article, we consider experimental situations where a blocked regular two-level fractional factorial initial design is used. We investigate the use of the semi-fold technique as a follow-up strategy for de-aliasing effects that are confounded in the initial design as well as an alternative method for constructing blocked fractional factorial designs. A construction method is suggested based on the full foldover technique and sufficient conditions are obtained when the semi-fold yields as many estimable effects as the full foldover.     

Classification Rules for Multivariate Repeated Measures Data with Equicorrelated Correlation Structure on Both Time and Spatial Repeated Measurements

We study the problem of classification for multivariate repeated measures data with structured correlations on both time and spatial repeated measurements. This is a very important problem in many biomedical as well as in engineering field. Classification rules as well as the algorithm to compute the maximum likelihood estimates of the required parameters are given.     

Efficiency of nonparametric tests for scale shift at an unknown time point

Recently Hsieh considered three nonparametric tests for the scalechange problem and showed them to be asymptotically as efficient as their parametric competitors. Here the class of so called sum-type statistics is studied which contains the statistics of Hsieh. It is proved that max-type statistics can be constructed that are at least as efficient in the sense of Bahadur as the sum-type statistics.     

Nonparametric approaches to testing and multiple comparisons in a one-way anova

Two questions of interest involving nonparametric multiple comparisons are considered. The first question concerns whether it is appropriate to use a multiple comparison procedure as a test of the equality of k treatments, and if it is, which procedure performs best as a test. Our results show that for smaller k values some multiple comparison procedures perform well as tests. The second question concerns whether a joint ranking or a separate ranking multiple comparison procedure performs better as a test and as a device for treatment separation. We find that the joint ranking procedure does slightly better as a test, but for treatment separation the answer depends on the situation.     

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology.     

Gaussian-based kernels

We derive a class of higher-order kernels for estimation of densities and their derivatives, which can be viewed as an extension of the second-order Gaussian kernel. These kernels have some attractive properties such as smoothness, manageable convolution formulae, and Fourier transforms. One important application is the higher-order extension of exact calculations of the mean integrated squared error. The proposed kernels also have the advantage of simplifying computations of common window-width selection algorithms such as least-squares cross-validation. Efficiency calculations indicate that the Gaussian-based kernels perform almost as well as the optimal polynomial kernels when die order of the derivative being estimated is low.     

A new confidence region for the multinomial distribution

A simple confidence region is proposed for the multinomial parameter. It is designed for situations having zero cell counts. Simulation studies as well as a real data application show that it performs at least as well as than at least two of the most common confidence regions.     

Variable selection by ensembles for the Cox model

Using Cox regression as the main platform, we study the ensemble approach for variable selection. We use a popular real-data example as well as simulated data with various censoring levels to illustrate the usefulness of the ensemble approach, and study the nature of these ensembles in terms of their strength and diversity. By relating these characteristics to the ensemble's selection accuracy, we provide useful insights for how to choose among different ensemble strategies, as well as guidelines for thinking about how to design more effective ensembles.     

Distance equalizers

Distance equalizers are introduced as empirical measures of central tendency that make distances to univariate data as similar as possible. These measures are made precise by means of various so-called fluctuation functions which account for distances in different ways. Distance equalizers differ from the mean as well as from the median. Also, distance equalizers relate to dispersion measures. Algorithms and closed-form solutions for special cases are given. Some computations require to perform multiextremal function minimization. Distance equalization is extendable to data from higher dimensions and to function quantization in signal processing.     

Generalized-order statistics with random indices

In this paper, we study the asymptotic behavior of general sequence of extreme, intermediate and central generalized-order statistics (gos), as well as dual generalized-order statistics (dgos), which are connected asymptotically with some regularly varying functions. Moreover, the limit distribution functions of gos, as well as dgos, with random indices, are obtained under general conditions.     

Efficiency of nonparametric tests for scale shift at an unknown time point

Recently Hsieh considered three nonparametric tests for the scale-change problem and showed them to be asymptotically as efficient as their parametric competitors. Here the class of so called sum-type statistics is studied which contains the statistics of Hsieh. It is proved that max-type statistics can be constructed that are at least as efficient in the sense of Bahadur as the sum-type statistics.     

Design of an attribute np control chart for process monitoring based on repetitive group sampling under truncated life tests

In this paper, an attribute control chart under repetitive group sampling is designed for monitoring the production process where the lifetime of the product is considered as quality of the product. We assume that the lifetime follows the Pareto distribution of second kind with known shape parameter. The performance of the proposed chart is evaluated by average run length. The control limits coefficients as well as the repetitive group sampling parameter such as sample size are determined such that the in-control average run length is as close as to the specified average run length. Out-of-control average run length is also reported for different shift constants with corresponding optimal parameters. In addition, performance of proposed control chart is compared with the performance of existing chart. An economical designing of proposed control chart is also discussed.     

Influence diagnostics for polyhazard models in the presence of covariates

In this paper, we present various diagnostic methods for polyhazard models. Polyhazard models are a flexible family for fitting lifetime data. Their main advantage over the single hazard models, such as the Weibull and the log-logistic models, is to include a large amount of nonmonotone hazard shapes, as bathtub and multimodal curves. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. A discussion of the computation of the likelihood displacement as well as the normal curvature in the local influence method are presented. Finally, an example with real data is given for illustration.     

信任危机、逆向选择与品牌重建——来自网贷之家的经验证据

以中国P2P行业信任危机为背景,借助网贷之家公布的数据,利用面板固定效应模型研究信任缺失情形下的逆向选择问题以及政府和行业协会重建品牌信任的效果,结果表明:P2P行业信任危机下的平台旨在通过建设品牌形象来吸引投资人与借款人的做法是徒劳的,信任缺失导致合规平台的诚信经营并不为公众所认可,长此以往这些平台很可能会退出市场,进而引发逆向选择问题;自上而下的制度建设在重建品牌信任中取得了可喜的成绩,有效缓解并逆转了P2P行业的逆向选择问题,且市场整治初见成效。此研究成果对于目前正在进行的P2P行业整顿具有十分现实的指导意义。     

Pareto Regression: A Bayesian Analysis

Abstract

In this article, a new model is presented that is based on the Pareto distribution of the second kind, when the location parameter depends on covariates as well as unobserved heterogeneity. Bayesian analysis of the model can be performed using Markov Chain Monte Carlo techniques. The new procedures are illustrated in the context of artificial data as well as international output data.