Robust designs for diallel crosses against the missing of one block

Dey & Midha (1996) showed that some of the complete diallel crosses plans, obtained by using triangular partially balanced designs with two associate classes, are optimal. In this investigation, it is derived that these optimal designs for diallel crosses are robust also against the unavailability of one block.     

Censored time series analysis with autoregressive moving average models

The authors consider time series observations with data irregularities such as censoring due to a detection limit. Practitioners commonly disregard censored data cases which often result in biased estimates. The authors present an attractive remedy for handling autocorrelated censored data based on a class of autoregressive and moving average (ARMA) models. In particular, they introduce an imputation method well suited for fitting ARMA models in the presence of censored data. They demonstrate the effectiveness of their technique in terms of bias, efficiency, and information loss. They also describe its adaptation to a specific context of meteorological time series data on cloud ceiling height, which are measured subject to the detection limit of the recording device.     

Estimation of regression vectors in linear mixed models with Dirichlet process random effects

The Dirichlet process has been used extensively in Bayesian non parametric modeling, and has proven to be very useful. In particular, mixed models with Dirichlet process random effects have been used in modeling many types of data and can often outperform their normal random effect counterparts. Here we examine the linear mixed model with Dirichlet process random effects from a classical view, and derive the best linear unbiased estimator (BLUE) of the fixed effects. We are also able to calculate the resulting covariance matrix and find that the covariance is directly related to the precision parameter of the Dirichlet process, giving a new interpretation of this parameter. We also characterize the relationship between the BLUE and the ordinary least-squares (OLS) estimator and show how confidence intervals can be approximated.     

On some dynamic generalized information measures for bivariate lifetimes

Several generalizations to the concept of Kullback-Leibler divergence measure and Kerridge inaccuracy measure are available in the literature. In a recent paper Kundu (Metrika, 78:415–435, 2015 Kundu, C. 2015. Generalized measures of information for truncated random variables. Metrika 78:415–35.[Crossref], [Web of Science ®] , [Google Scholar]) considered a generalized K-L divergence measure of order (α, β). Nath (Metrika, 13:123–135, 1968 Nath, P. 1968. Inaccuracy and coding theory. Metrika 13:123–35.[Crossref] , [Google Scholar]) has also proposed generalized inaccuracy measure of order α. Here we address the question of extending these measures to higher dimensions with reference to residual lifetimes. In the present work, the generalized divergence and inaccuracy measures are extended for conditional lifetimes of two components having possibly different ages. Several properties, including monotonicity, and bounds of these measures are obtained for conditional random variables. Moreover, we study the effect of (increasing) monotone transformation on these generalized measures.     

Interpenetrating subsampling regression estimation with or without jackknifing

In this paper four regression estimators are considered for a finite population total based on interpenetrating subsamples, two of which are with jackknifing and the other two are without jackknifing. Both theoretical and empirical comparisons of the four proposed estimators are done with respect to bias, variance and mean square error.     

Selection of Binary Variables and Classification by Boosting

We adopt boosting for classification and selection of high-dimensional binary variables for which classical methods based on normality and non singular sample dispersion are inapplicable. Boosting seems particularly well suited for binary variables. We present three methods of which two combine boosting with the relatively classical variable selection methods developed in Wilbur et al. (2002 Wilbur , J. D. , Ghosh , J. K. , Nakatsu , C. H. , Brouder , S. M. , Doerge , R. W. ( 2002 ). Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints . Biometrics 58 : 378 – 386 . [Google Scholar]). Our primary interest is variable selection in classification with small misclassification error being used as validation of proposed method for variable selection. Two of the new methods perform uniformly better than Wilbur et al. (2002 Wilbur , J. D. , Ghosh , J. K. , Nakatsu , C. H. , Brouder , S. M. , Doerge , R. W. ( 2002 ). Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints . Biometrics 58 : 378 – 386 . [Google Scholar]) in one set of simulated and three real life examples.     

Structure of Uncertainty and Decision Making: An Experimental Investigation*

Decisions in the real world usually involve imprecise information or uncertainty about the precesses by which outcomes may be determined. This research reports the results of a laboratory experiment which examined whether the structure of uncertainty, namely, both the center and the range of the probability distribution describing the uncertainty, is an important determinant of choice. Specifically, it examines how the uncertainty of audit by the Internal Revenue Service of income tax returns affects taxpayers' decisions about intentional noncompliance. The context is relevant as almost nothing is known about how taxpayers assess detection risks using the probability information they have. The study focuses on intentional noncompliance. The factors affecting it are distinct and separate from those affecting unintentional noncompliance. Other factors that affect intentional tax noncompliance, such as risk, tax rates, and penalty rates, were controlled in the experiment. It was hypothesized that the lower the mean and the lesser the range (ambiguity) of the perceived audit probability, the greater the international noncompliance. As hypothesized, the analysis indicates that both the mean and the range of the perceived audit probability rate affect intentional noncompliance, though the effect of ambiguity is greater at a relatively higher level of mean. This result suggests that the strength of the information describing an uncertain event is captured better by both the mean and the range of the uncertainty than either of those components singly.     

The Effects of Knowledge Management on Team Members' Ratings of Project Success and Impact*

Many organizations are trying to improve the generation and utilization of knowledge. The activities associated with these efforts are identified as organizational knowledge management (KM). While much has been written about knowledge management from the organizational level, the success of such efforts over the long run will depend upon on how KM activities impact important outcomes as perceived by those at the employee level who actually implement the activities. This study used the input‐process‐output framework of team effectiveness to investigate the relationship between selected KM‐related activities on integrated product and process development team members' satisfaction with their project's success and the impact they expected it to have on the organization. The results indicate that team‐level leadership and support (i.e., inputs), along with knowledge generation and dissemination (i.e., processes), are key drivers of member performance‐related ratings (i.e., outputs). Finally, and possibly most importantly, a number of interactions were evident suggesting that the KM processes moderate the effects of the KM inputs. These findings and their implications are discussed.