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## Slepian Wavelet Variances for Regularly and Irregularly Sampled Time Series

### Statistical Challenges in Modern Astronomy V (2012-01-01): 902 , January 01, 2012

We discuss approximate scale-based analysis of variance for Gaussian time series based upon Slepian wavelets. These wavelets arise as eigenfunctions of an energy maximization problem in a pass band of frequencies. Unlike the commonly used Daubechies wavelets, Slepian wavelets have the ability to accommodate both regularly and irregularly sampled data. For regularly sampled Gaussian time series, we derive statistical theory for Slepian-based wavelet variances and show that it is comparable to Daubechies-based variances. For irregularly sampled time series data, we derive a corresponding statistical theory for Slepian-based wavelet variances. We demonstrate its use on X-ray fluctuations from a binary star system and on a light curve from the variable star Z UMa.

## Estimation of Parameters in Factorial Triallel Analysis for BIB Design — the Mixed Model

### MODA4 — Advances in Model-Oriented Data Analysis (1995-01-01): 151-156 , January 01, 1995

The paper presents the estimation some genetic parameters concerning hybrids obtained in factorial triallel crossing system. The hybrids are compared in a balanced incomplete block design in which the block effects are treated as random. The statistical analysis includes estimation (intra-block, inter-block and combining) of general line effects and two-line and three-line specific effects.

## The use of Contour Plots for Interpretation of Multi-Drug Combination

### Medical Informatics Europe ’90 (1990-01-01) 40: 45-47 , January 01, 1990

### Summary

A combination of more than two drugs makes it difficult to find the optimal treatment level. One way to analyse such combinations is through Regression analysis. To help in the analysis various kinds of contour plots can be generated from the Regression model used. They can be very helpful in the interpretation of the multi-relations-ships occured by the drug combinations. Where the number of interactions grows exponentially with the number of drugs used in the treatment. However, by considering combinations of constant response, the dimensions can be decreased by one, marking it easier to interpret the relationships existing between all drugs in the combination.

## Spatial Regression Using Kernel Averaged Predictors

### Journal of Agricultural, Biological, and Environmental Statistics (2011-06-01) 16: 233-252 , June 01, 2011

Traditional spatial linear regression models assume that the mean of the response is a linear combination of predictors measured at the same location as the response. In spatial applications, however, it seems plausible that neighboring predictors can also inform about the response. This article proposes using unobserved kernel averaged predictors in such regressions. The kernels are parametric introducing additional parameters that are estimated with the data. Properties and challenges of using kernel averaged predictors within a regression model are detailed in the simple case of a univariate response and a single predictor. Additionally, extensions to multiple predictors and generalized linear models are discussed. The methods are demonstrated using a data set of dew duration and shrub density. Supplemental materials are available online.

## Risks of Inconclusiveness

### Phase II Clinical Development of New Drugs (2017-01-01): 131-143 , January 01, 2017

During the entire clinical development of a new medicinal product, there are lots of milestones and decision points. Many of these key decisions could have long-term impact and involve a large amount of resources and investment. As indicated in previous chapters, the Go/NoGo decision after a PoC study is one of such examples.

## A note on the asymptotic normality of the distribution of the number of empty cells in occupancy problems

### Annals of the Institute of Statistical Mathematics (1971-12-01) 23: 507-513 , December 01, 1971

## Discriminant Analysis Based on Continuous and Discrete Variables

### Statistical Methods for Biostatistics and Related Fields (2007-01-01): 3-27 , January 01, 2007

## Money supply and credit

### Financial Statistics (2009-12-01) 572: 53-76 , December 01, 2009

## Optimal approximate designs for comparison with control in dose-escalation studies

### TEST (2017-09-01) 26: 638-660 , September 01, 2017

Consider an experiment, in which a new drug is tested for the first time on human subjects, namely healthy volunteers. Such experiments are often performed as dose-escalation studies: a set of increasing doses is preselected; individuals are grouped into cohorts; and in each cohort, dose number *i* can be administered only if dose number
$$i-1$$
has already been tested in the previous cohort. If an adverse effect of a dose is observed, the experiment is stopped, and thus, no subjects are exposed to higher doses. In this paper, we assume that the response is affected both by the dose or placebo effects and by the cohort effects. We provide optimal approximate designs for estimating the effects of the drug doses compared with the placebo with respect to selected optimality criteria (*E*-, *MV*- and *LV*-optimality). In particular, we prove the optimality of the so-called Senn designs with respect to all of the studied optimality criteria, and we provide optimal extensions of these designs for selected criteria.