Estimation of a response surface from-a general incomplete block design

If an incomplete block design is used to compare v treatments and later it is decided to fit a second order response surface using the fact that the v treatments are v combinations of levels of m different factors, how will one proceed to obtain this? This paper demonstrates how the usual analysis of an incomplete block design with or without recovery of interblock information can be augmented to yield estimates of response surface coefficients.     

西安市天然气阶梯价格城市家庭门槛消费量的估计

以西安市为例,通过分层随机抽样获取样本,对家庭天然气阶梯定价初始档的消费量及决定因素进行分析。通过调研发现,在燃气灶、燃气热水器和壁挂锅炉三个主要用能终端中,壁挂锅炉家庭和非壁挂锅炉家庭的消费特征显著不同。运用He和Reiner的能源消费门槛值(Threshold)模型,对这两类家庭样本进行估计,得到壁挂锅炉和非壁挂锅炉家庭天然气阶梯价格门槛值消费量分别为898.07立方米/年和182.74立方米/年,证实西安市实施中的天然气阶梯价格划定的门槛值(一档线)过高,不能实现高收入家庭交叉补贴低收入家庭的收入再分配效果。     

The Design of Scoring Schemes for Surveys Using the Block Total Response Method

ABSTRACT

In this article we investigate the design of scoring schemes for surveys using the block total response method. This method was first proposed by Raghavarao and Federer (1979 Raghavarao , D. , Federer , W. T. ( 1979 ). Block total response as an alternative to the randomized response method in surveys . J. Roy. Statist. Soc. 41 ( 1 ): 40 – 45 . [CSA] [CROSSREF]  [Google Scholar]) to provide accurate estimates of the base rates of sensitive characteristics using balanced incomplete block designs. The scoring scheme used in Raghavarao and Federer (1979 Raghavarao , D. , Federer , W. T. ( 1979 ). Block total response as an alternative to the randomized response method in surveys . J. Roy. Statist. Soc. 41 ( 1 ): 40 – 45 . [CSA] [CROSSREF]  [Google Scholar]) did not guarantee anonymity of answers and so the possibility of improving on this basic scoring scheme is considered in this article.     

Model selection in randomized response techniques for binary responses

Randomized response techniques (RRTs) have been proposed in survey sampling literature as a solution to the problem of social desirability bias (SDB) while dealing with sensitive questions. All RRTs reduce the SDB by introducing privacy protection for the respondents, but the variances of the estimates become larger compared with the ones obtained from direct questioning technique (DQT). The success of RRTs heavily depends on the assumption that the variable of interest is in fact sensitive for the population under study. There might be situations, however, where a presumably sensitive question is not considered to be sensitive in some populations, in which case using an RRT instead of the DQT would inflate the variance of the estimates unreasonably. In this study, we propose a two-stage sampling procedure for a binary response, which enables one to accurately estimate both the prevalence of the sensitive characteristic and the probability of cheating in a population. The proposed model allows one to choose between an RRT and the DQT. We support our theoretical results with numerous simulations.     

On the unbiased estimation of fixed effects in a mixed model for growth curves

A mixed model for growth curves is introduced. A method of estimating the variance components is indicated. It is shown that the generalised least squares estimates of the fixed effects using estimates of variance components are unbiased and an expression is obtained for the increase in variance due to the substitution of the variances by their estimates. These results are directly applied to designs with two-way elimination of heterogeneity.     

On the construction of group divisible incomplete block designs

In this paper a method of constructing group-divisible incomplete block designs has been suggested. A series of balanced incomplete block designs has also been obtained.     

Estimating a distribution for each category of a sensitive variate

The use of a Randomized Response (RR) design makes it possible to estimate the distribution of a sensitive variate. In this paper, the estimation of the distribution of a non-sensitive variate for each category of a sensitive variate is considered for the case where data on the sensitive variate is obtained by use of an RR procedure. Simple estimators are developed without making any distributional assumptions about the non-sensitive variate. However, if distributional assumptions are made, it is shown that the EM algorithm may be used to compute Maximum Likelihood estimates. Computational comparisons of the estimators, using simulation, indicate that the simple estimators perform well, particularly for large sample sizes.     

Combining estimates from multiple early studies to obtain estimates of response: using shrinkage estimates to obtain estimates of response

Development of anti-cancer therapies usually involve small to moderate size studies to provide initial estimates of response rates before initiating larger studies to better quantify response. These early trials often each contain a single tumor type, possibly using other stratification factors. Response rate for a given tumor type is routinely reported as the percentage of patients meeting a clinical criteria (e.g. tumor shrinkage), without any regard to response in the other studies. These estimates (called maximum likelihood estimates or MLEs) on average approximate the true value, but have variances that are usually large, especially for small to moderate size studies. The approach presented here is offered as a way to improve overall estimation of response rates when several small trials are considered by reducing the total uncertainty.The shrinkage estimators considered here (James-Stein/empirical Bayes and hierarchical Bayes) are alternatives that use information from all studies to provide potentially better estimates for each study. While these estimates introduce a small bias, they have a considerably smaller variance, and thus tend to be better in terms of total mean squared error. These procedures provide a better view of drug performance in that group of tumor types as a whole, as opposed to estimating each response rate individually without consideration of the others. In technical terms, the vector of estimated response rates is nearer the vector of true values, on average, than the vector of the usual unbiased MLEs applied to such trials.     

ESTIMATING A LOGIT MODEL WITH RANDOMIZED DATA: THE CASE OF COCAINE USE

In his influential book, Maddala (1983) suggests combining randomized response survey data with other personal information to estimate logit models predicting immoral, unpopular, or unlawful behaviour. This study is one of the first to implement this technique using real data. Models of college students' recent cocaine use are estimated with academic performance and socio-economic characteristics as determinants. Parameter estimates obtained from randomized response surveys are compared to those obtained using conventional, direct question surveys. The results indicate that randomized response estimates provide useful information on the degree to which inferences regarding the determinants of cocaine use are sensitive to survey type.     

A note on the estimation methods of crop production at the block level

This paper presents a method of estimation of crop-production statistics at smaller geographical levels like a community development block (generally referred to as a block) to make area-specific plans for agricultural development programmes in India. Using available district-level data on crop yield from crop-cutting experiments and data on auxiliary variables from various administrative sources, a suitable regression model is fitted. The fitted model is then used to predict the crop production at the block level. Some scaled estimators are also developed using predicted estimates. An empirical study is also carried out to judge the merits of the proposed estimators.     

Remove unwanted variation retrieves unknown experimental designs

Remove unwanted variation (RUV) is an estimation and normalization system in which the underlying correlation structure of a multivariate dataset is estimated from negative control measurements, typically gene expression values, which are assumed to stay constant across experimental conditions. In this paper we derive the weight matrix which is estimated and incorporated into the generalized least squares estimates of RUV-inverse, and show that this weight matrix estimates the average covariance matrix across negative control measurements. RUV-inverse can thus be viewed as an estimation method adjusting for an unknown experimental design. We show that for a balanced incomplete block design (BIBD), RUV-inverse recovers intra- and interblock estimates of the relevant parameters and combines them as a weighted sum just like the best linear unbiased estimator (BLUE), except that the weights are globally estimated from the negative control measurements instead of being individually optimized to each measurement as in the classical, single measurement BIBD BLUE.     

On choosing between fixed and random block effects in some no-interaction models

The purpose of this article is to strengthen the understanding of the relationship between a fixed-blocks and random-blocks analysis in models that do not include interactions between treatments and blocks. Treating the block effects as random has been recommended in the literature for balanced incomplete block designs (BIBD) because it results in smaller variances of treatment contrasts. This reduction in variance is large if the block-to-block variation relative to the total variation is small. However, this analysis is also more complicated because it results in a subjective interpretation of results if the block variance component is non-positive. The probability of a non-positive variance component is large precisely in those situations where a random-blocks analysis is useful – that is, when the block-to-block variation, relative to the total variation, is small. In contrast, the analysis in which the block effects are fixed is computationally simpler and less subjective. The loss in power for some BIBD with a fixed effects analysis is trivial. In such cases, we recommend treating the block effects as fixed. For response surface experiments designed in blocks, however, an opposite recommendation is made. When block effects are fixed, the variance of the estimated response surface is not uniquely estimated, and in practice this variance is obtained by ignoring the block effect. It is argued that a more reasonable approach is to treat the block effects to be random than to ignore it.     

Alternative models for the heterogeneity of mortality risks among the aged

The authors examine how sensitive the estimates of heterogeneity in the mortality risks in a population are to the choices of two types of function, "one describing the age-specific rate of increase of mortality risks for individuals and the other describing the distribution of mortality risks across individuals." U.S. data from published Medicare mortality rates for the period 1968-1978 are used to analyze total mortality among the aged. "In addition, national vital statistics data for the period 1950-1977 were used to analyze adult lung cancer mortality. For these data, the estimates of structural parameters were less sensitive to reasonable choices of the heterogeneity distribution (gamma vs. inverse Gaussian) than to reasonable choices of the hazard rate function (Gompertz vs. Weibull)."     

An extension of Franklin's randomized response technique for multi-proportions

ABSTRACT

In this article, we introduce six estimators, three based on row averages and the remaining three on column averages of population proportions for trichotomous population when randomized response sampling with a normal randomizing distribution is used. The estimators have been obtained using the method of moments. All the proposed estimators are shown to be unbiased and their variances have been worked out. The percent relative efficiencies of the column total based estimators with respect to row total based estimators are investigated through empirical study.     

A bootstrap approximation to the joint distribution of sum and maximum of a stationary sequence

This paper establishes the asymptotic validity for the moving block bootstrap as an approximation to the joint distribution of the sum and the maximum of a stationary sequence. An application is made to statistical inference for a positive time series where an extreme value statistic and sample mean provide the maximum likelihood estimates for the model parameters. A simulation study illustrates small sample size behavior of the bootstrap approximation.     

On the block structure of proper block designs

In this paper we consider proper block designs and derive an upper bound for the number of blocks which can have a fixed number of symbols common with a given block of the design. To arrive at the desired bound, a generalization of an integer programming theorem due to Bush (1976) is first obtained. The integer programming theorem is then used to derive the main result of this paper. The bound given here is then compared with a similar bound obtained by Kageyama and Tsuji (1977).     

On Block Updating in Markov Random Field Models for Disease Mapping

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely poor due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different applications: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. Implementation of such block algorithms is relatively easy using methods for fast sampling of Gaussian Markov random fields ( Rue, 2001 ). By comparison, Monte Carlo estimates based on single-site updating can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components.     

Parametric Inference in Stationary Time Series Models with Dependent Errors

This article is concerned with inference for the parameter vector in stationary time series models based on the frequency domain maximum likelihood estimator. The traditional method consistently estimates the asymptotic covariance matrix of the parameter estimator and usually assumes the independence of the innovation process. For dependent innovations, the asymptotic covariance matrix of the estimator depends on the fourth‐order cumulants of the unobserved innovation process, a consistent estimation of which is a difficult task. In this article, we propose a novel self‐normalization‐based approach to constructing a confidence region for the parameter vector in such models. The proposed procedure involves no smoothing parameter, and is widely applicable to a large class of long/short memory time series models with weakly dependent innovations. In simulation studies, we demonstrate favourable finite sample performance of our method in comparison with the traditional method and a residual block bootstrap approach.     

A Practical Implementation and Bayesian Estimation in Franklin's Randomized Response Procedure

A Bayesian estimator based on Franklin's randomized response procedure is proposed for proportion estimation in surveys dealing with a sensitive character. The method is simple to implement and avoids the usual drawbacks of Franklin's estimator, i.e., the occurrence of negative estimates when the population proportion is small. A simulation study is considered in order to assess the performance of the proposed estimator as well as the corresponding credible interval.     

OPTIMUM SINGULAR SPRING BALANCE WEIGHING DESIGNS WITH NON-HOMOGENEITY OF THE VARIANCES OF ERRORS FOR ESTIMATING THE TOTAL WEIGHT

The problem of estimation of the total weight of objects using a singular spring balance weighing design with non-homogeneity of the variances of errors has been dealt with in this paper. Based on a theorem by Katulska (1984) giving a lower bound for the variance of the estimated total weight, a necessary and sufficient condition for this lower bound to be attained is obtained. It is shown that weighing designs for which the the lower bound is attainable, can be constructed from the incidence matrices of (α₁,.,α_t)-resolvable block designs, α-resolvable block designs, singular group divisible designs, and semi-regular group divisible designs.