Confidence intervals for the mean of a population containing many zero values under unequal‐probability sampling

In many applications, a finite population contains a large proportion of zero values that make the population distribution severely skewed. An unequal‐probability sampling plan compounds the problem, and as a result the normal approximation to the distribution of various estimators has poor precision. The central‐limit‐theorem‐based confidence intervals for the population mean are hence unsatisfactory. Complex designs also make it hard to pin down useful likelihood functions, hence a direct likelihood approach is not an option. In this paper, we propose a pseudo‐likelihood approach. The proposed pseudo‐log‐likelihood function is an unbiased estimator of the log‐likelihood function when the entire population is sampled. Simulations have been carried out. When the inclusion probabilities are related to the unit values, the pseudo‐likelihood intervals are superior to existing methods in terms of the coverage probability, the balance of non‐coverage rates on the lower and upper sides, and the interval length. An application with a data set from the Canadian Labour Force Survey‐2000 also shows that the pseudo‐likelihood method performs more appropriately than other methods. The Canadian Journal of Statistics 38: 582–597; 2010 © 2010 Statistical Society of Canada     

New Tests of Spatial Segregation Based on Nearest Neighbour Contingency Tables

Abstract.  The spatial clustering of points from two or more classes (or species) has important implications in many fields and may cause segregation or association, which are two major types of spatial patterns between the classes. These patterns can be studied using a nearest neighbour contingency table (NNCT) which is constructed using the frequencies of nearest neighbour types. Three new multivariate clustering tests are proposed based on NNCTs using the appropriate sampling distribution of the cell counts in a NNCT. The null patterns considered are random labelling (RL) and complete spatial randomness (CSR) of points from two or more classes. The finite sample performance of these tests are compared with other tests in terms of empirical size and power. It is demonstrated that the newly proposed NNCT tests perform relatively well compared with their competitors and the tests are illustrated using two example data sets.     

Comparison of estimators for heteroskedastic models

Ordinary least squares (OLS) yield inefficient parameter estimates and inconsistent estimates of the covariance matrix in case of heteroskedastic errors. Robinson's adaptive estimator and the Cragg estimator avoid any explicit parameterization of heteroskedasticity, and reduce the danger of misspecification. A small Monte Carlo experiment is performed to compare the behavior of the adaptive estimator with the performance of the Cragg estimator. The Monte Carlo experiment includes simulations of the Generalized Least Squares (GLS) estimator. Indeed, an interesting question is how more sophisticated techniques, like the adaptive estimator, compare with GLS when the latter relies on an incorrect specification of the heteroskedastic process. It turns out that the regression parameters, when estimated adaptively, display small mean squared errors and great efficiency in case of medium or high heteroskedasticity. The covariance matrix, instead, is better estimated by the Cragg estimator or by GLS based on a misspecified error term, since the adaptive estimator overpredicts the standard errors of the regression parameters.     

Orthogonalized Residuals for Estimation of Marginally Specified Association Parameters in Multivariate Binary Data

Abstract. This paper focuses on marginal regression models for correlated binary responses when estimation of the association structure is of primary interest. A new estimating function approach based on orthogonalized residuals is proposed. A special case of the proposed procedure allows a new representation of the alternating logistic regressions method through marginal residuals. The connections between second‐order generalized estimating equations, alternating logistic regressions, pseudo‐likelihood and other methods are explored. Efficiency comparisons are presented, with emphasis on variable cluster size and on the role of higher‐order assumptions. The new method is illustrated with an analysis of data on impaired pulmonary function.     

On Universal Optimality of Circular Weakly Neighbor Balanced Designs Under an Interference Model

In this article, the optimal design problem in a fixed effects interference model with left-neighbor effects is studied. It is known (Druilhet, 1999 Druilhet , P. ( 1999 ). Optimality of circular neighbor balanced designs . J. Statist. Plann. Infer. 81 : 141 – 152 .[Crossref], [Web of Science ®] , [Google Scholar]) that circular neighbor balanced designs (CNBDs) are universally optimal in such a model. We prove the universal optimality of circular weakly neighbor balanced designs (CWNBDs), which require a smaller number of blocks than CNBDs. CWNBDs with the number of blocks smaller than the number of treatments belong to the class of partially neighbor balanced designs (PNBDs) defined by Wilkinson et al. (1983 Wilkinson , G. N. , Eckert , R. , Hancock , T. W. , Mayo , O. ( 1983 ). Nearest neighbour (NN) analysis of field experiments . J. Roy. Statist. Soc. Ser. B 45 : 151 – 210 . [Google Scholar]). We give a construction method for some CWNBDs, with examples.     

Minimax designs for approximately linear models with AR(1) errors

We obtain designs for linear regression models under two main departures from the classical assumptions: (1) the response is taken to be only approximately linear, and (2) the errors are not assumed to be independent, but to instead follow a first-order autoregressive process. These designs have the property that they minimize (a modification of) the maximum integrated mean squared error of the estimated response, with the maximum taken over a class of departures from strict linearity and over all autoregression parameters ρ,|ρ,| < 1, of fixed sign. Specific methods of implementation are discussed. We find that an asymptotically optimal procedure for AR(1) models consists of choosing points from that design measure which is optimal for uncorrelated errors, and then implementing them in an appropriate order.     

Sex change and spatial distribution pattern in an intertidal holothurian Polycheira rufescens in the reproductive season

The patterns of sex change and spatial distribution in an intertidal holothurian, Polycheira rufescens (Chiridotidae; Echinodermata), were investigated on a stony beach in Amakusa, western Kyushu. Field caging experiments revealed that some individuals of P. rufescens underwent a sequential sex change from male to female via hermaphrodite stages and back to male again within a single reproductive season. The sex ratio of the population gradually changed from male dominance at first to equal proportions of males and females as the reproductive season progressed. Toward the end of the reproductive season, immature or spent individuals increased in proportion. Stone size appeared to be an important factor affecting the occurrence of P. rufescens individuals on a stony beach. Analysis of spatial distribution by means of Morisita's index of dispersion and nearest neighbor distances indicated that (i) males showed a slightly stronger tendency to aggregate than females, while females had a tendency toward uniform distribution; (ii) females tended to attract males, as shown by relatively short female-to-male distances; and (iii) there was a substantial variation in male-to-female distances, such that some males were positioned close to females while others were not. It may be suggested that spatial distribution of P. rufescens individuals during the reproductive period is partly dictated by the differential needs of individuals of different sexual states. Received: January 25, 1999 / Accepted: June 10, 1999     

The Frisch Elasticity in the Mercosur Countries: A Pseudo‐Panel Approach

In estimating the Frisch elasticity – i.e., the elasticity of substitution between worked hours and real wages, holding constant the marginal utility of wealth – for the Mercosur countries, this article finds a strong heterogeneity. It argues that severe liquidity constraints are the main reason for the negative estimates and that the heterogeneity is the outcome of differences in many relevant economic dimensions – ranging from sectorial specialisation to welfare‐state provisions and labour‐market specificities. The diversity of Frisch elasticities calls for the development of a cross‐country (rather than a within‐country) policy approach, since they crucially affect the dynamics of the business cycle, the synchronisation of which is a step prior to the design of macro‐convergence policies in the Mercosur context.     

The Construction of Optimal Balanced Incomplete Block Designs When Adjacent Observations are Correlated

An algorithm is described for the optimal rearrangement of the treatments within each block of a Balanced Incomplete Block Design when a specified “nearest neighbour” correlation structure exists among observations from plots in the same block. The procedure uses results obtained by Kiefer & Wynn (1981). Designs obtained using the algorithm are found to compare favourably with those produced by combinatorial methods given in Cheng (1983). The algorithm produces optimal designs for all BIBD parameter sets, including those not covered by the results of Kiefer & Wynn or Cheng.     

网络拟态空间的意识形态治理路径

大数据、云时代的到来,将人们从现实空间延伸到拟态空间,拟态空间是一种对人们“观念世界”和“意识世界”产生深刻影响的象征化、拟似化的虚拟空间,网络拟态空间具有“难控性”与“可控性”的二重性.网络拟态空间的治理应该从统筹布局、完善机制、内容建设、议程设置、环境建设等方面保障主体参与治理的原动力、调动网民参与治理的源动力、增进主流意识形态的吸引力、强化主流意识形态的引导力、形成主流意识形态的约束力,最终形成网络拟态空间意识形态的治理合力,达到“善治”的目标.