Asymptotic Behavior of the Hazard Rate Kernel Estimator Under Truncated and Censored Data

In this article, we give the asymptotic mean integrated squared error and the mean squared error for the kernel estimator of the hazard rate from truncated and censored data. Martingale techniques and combinatory calculus are used to obtain these results. A probability bound and the optimal bandwidth choice are also given.     

M-Estimators Based on Inverse Probability Weighted Estimating Equations with Response Missing at Random

Asymptotic properties of M-estimators with complete data are investigated extensively. In the presence of missing data, however, the standard inference procedures for complete data cannot be applied directly. In this article, the inverse probability weighted method is applied to missing response problem to define M-estimators. The existence of M-estimators is established under very general regularity conditions. Consistency and asymptotic normality of the M-estimators are proved, respectively. An iterative algorithm is applied to calculating the M-estimators. It is shown that one step iteration suffices and the resulting one-step M-estimate has the same limit distribution as in the fully iterated M-estimators.     

Statistics Problems with Simple Numbers

This article concerns the construction of simple numerical illustrations of statistical techniques for use in introductory classes. Minimizing the amount of calculation facilitates checking, promotes reliability, is quicker, and reinforces the confidence of the student. Methods are described for generating (a) samples of size 3 upwards with integer-valued means and standard deviations, and (b) simple linear regressions with integer-valued intercepts and integer-valued or simple fractional slopes. Extensions to give exact pooled standard deviations in the two-sample problem and simple exact fractional correlation coefficients are also indicated. Further statistical procedures amenable to the same general approach are listed.     

A structured framework for assessing sensitivity to missing data assumptions in longitudinal clinical trials

The objective of this research was to demonstrate a framework for drawing inference from sensitivity analyses of incomplete longitudinal clinical trial data via a re‐analysis of data from a confirmatory clinical trial in depression. A likelihood‐based approach that assumed missing at random (MAR) was the primary analysis. Robustness to departure from MAR was assessed by comparing the primary result to those from a series of analyses that employed varying missing not at random (MNAR) assumptions (selection models, pattern mixture models and shared parameter models) and to MAR methods that used inclusive models. The key sensitivity analysis used multiple imputation assuming that after dropout the trajectory of drug‐treated patients was that of placebo treated patients with a similar outcome history (placebo multiple imputation). This result was used as the worst reasonable case to define the lower limit of plausible values for the treatment contrast. The endpoint contrast from the primary analysis was ? 2.79 (p = .013). In placebo multiple imputation, the result was ? 2.17. Results from the other sensitivity analyses ranged from ? 2.21 to ? 3.87 and were symmetrically distributed around the primary result. Hence, no clear evidence of bias from missing not at random data was found. In the worst reasonable case scenario, the treatment effect was 80% of the magnitude of the primary result. Therefore, it was concluded that a treatment effect existed. The structured sensitivity framework of using a worst reasonable case result based on a controlled imputation approach with transparent and debatable assumptions supplemented a series of plausible alternative models under varying assumptions was useful in this specific situation and holds promise as a generally useful framework. Copyright © 2012 John Wiley & Sons, Ltd.     

Analyzing Cohort Mortality Data

Several methods for analyzing data from mortality studies of occupationally or environmentally exposed cohorts are shown to be special cases of a single procedure. The procedure assumes a proportional hazards model for exposure effects and represents the log-likelihood kernel for the data as that of N independent Poisson variates, where N is the total number of person-units of mortality observation time in the study. It formalizes and justifies the epidemiological techniques of classifying deaths and person-months of study time into categories defined by exposure and other covariates, and of computing standardized mortality ratios and indirectly standardized death rates. Parameters representing exposure effects can be estimated by using standard software packages. Special cases and applications are described in the context of lung cancer mortality among U.S. uranium miners.     

Rethinking Print Periodicals Holdings Retention Policy in Small Academic Libraries

Abstract

Collection development policies in small academic libraries may lack a formal policy statement about print periodical holdings retention. However, there is a need for a distinct policy about print periodicals holdings and their retention. Periodicals collections at academic libraries have been greatly affected by publishers’ decisions to discontinue print journal formats and move to online-only electronic versions. The move from one format to another produces challenges to the retention of an effective print periodicals collection. Given these continuous changes in publication format, it is necessary for academic libraries to rethink their print periodicals holdings retention. This article will present a literature review on and case study of periodicals collection management and explore strategies for developing holdings policies and guidelines for retention. It will argue that collection development policies ought to include a separate policy for the print periodicals collection and that unlike their reputation for being time-consuming and inflexible, periodical retention policies can improve flexibility and guide in decision making, helping to preserve core titles and acquire new titles that support academic programs and the work of the college community.     

Preserving Open Access

Abstract

Libraries have an established track record of preserving the scholarly record, but this stewardship role has generally been exercised over materials the library has purchased or otherwise acquired. With open access publishing, the library’s role in preservation is less certain, as libraries may facilitate access to a wide array of freely accessible materials without ever obtaining any copies. Additionally, the open access landscape includes many small noncommercial publishers with limited resources that may not be able to invest in long term preservation and access. In this column, Courtney McAllister, Electronic Resources Librarian at Lillian Goldman Law Library, Yale University, discusses preservation concerns and strategies for open access publications and how libraries may be affected by unstable or unpredictable long-term access.     

Segmentation,alignment and statistical analysis of biosignals with application to disease classification

We present a novel methodology for a comprehensive statistical analysis of approximately periodic biosignal data. There are two main challenges in such analysis: (1) the automatic extraction (segmentation) of cycles from long, cyclostationary biosignals and (2) the subsequent statistical analysis, which in many cases involves the separation of temporal and amplitude variabilities. The proposed framework provides a principled approach for statistical analysis of such signals, which in turn allows for an efficient cycle segmentation algorithm. This is achieved using a convenient representation of functions called the square-root velocity function (SRVF). The segmented cycles, represented by SRVFs, are temporally aligned using the notion of the Karcher mean, which in turn allows for more efficient statistical summaries of signals. We show the strengths of this method through various disease classification experiments. In the case of myocardial infarction detection and localization, we show that our method compares favorably to methods described in the current literature.     

On time-irreversibility and other non-linear features in time series

Time-irreversibility, asymmetry of the distribution, and the occurrence of sudden bursts are considered, amongst others, as non-linear features in time series modeling. The implication is often made that time series showing these features must be analyzed using non-linear models. In contrast, this paper shows that time-irreversible asymmetric time series showing certain types of sudden bursts may be generated by linear models with adequate input sequences. Thus some non-linear time series features may be caused by the pattern in the input sequence rather than by non-linearity in the model. Examples are considered to illustrate the situation.     

Heterogeneous data modeling with two-component Weibull–Poisson distribution

The mixture distribution models are more useful than pure distributions in modeling of heterogeneous data sets. The aim of this paper is to propose mixture of Weibull–Poisson (WP) distributions to model heterogeneous data sets for the first time. So, a powerful alternative mixture distribution is created for modeling of the heterogeneous data sets. In the study, many features of the proposed mixture of WP distributions are examined. Also, the expectation maximization (EM) algorithm is used to determine the maximum-likelihood estimates of the parameters, and the simulation study is conducted for evaluating the performance of the proposed EM scheme. Applications for two real heterogeneous data sets are given to show the flexibility and potentiality of the new mixture distribution.