央地互动、多重逻辑与制度变迁——基于对建设用地一级配置层面的考察

以社会行动者理论与多重制度逻辑框架为基础,从中央政府与地方政府两个行动者的多重逻辑出发,在央地互动视阈下对中国建设用地一级配置管理的政策演化与制度逻辑进行了解析。研究发现,在建设用地一级配置管理的制度变迁中,央地双方在多重逻辑的相互作用下,针对不同的土地利用计划指标类型,实质设立了差别化的行动准则：在中央权威有效性逻辑与地方代理自利性和局部自主性多重考量的相互回应下,对于新增建设用地计划指标,央地采取了掌控与突破的相斥性准则;在中央强公共性和有效性逻辑重叠与地方局部自主性逻辑的相互交织下,对于非新增建设用地计划指标,央地则采取了放权与执行的相容性准则。在这种差别化的行动准则下,央地双方展开了持续性互动,最终形成了新增建设用地指标愈发收紧的“计划管控”、非新增建设用地计划指标愈发灵活的“市场调剂”的“双轨化”变迁路径。由此可见,在央地政府这种差别化的行动准则下,中国未来的建设用地配置改革,不能只寄期望于诸如简单引入市场机制等治理结构层面的改变,还需要从深层次把握中央集权体制下央地政府的多重逻辑、管理诉求与互动策略,由此才能设计能够引导央地激励相容、优势互补的制度改进机制。     

二语作格动词语义加工中的身物交互效应

This article investigates the theoretical basis and feasibility in the processing of English ergative verbs from the perspective of body-object interaction effects (BOI). It analyzes the motor degree of different verbs, probing into the semantic features and syntactic behaviors of ergative verbs. It also explores the relationships between the BOI effects and different type of verbs in semantic processing, examining the motor prominence of English ergative verbs, as well as analyzing the factors that cause the misusage of English ergative verbs in the second language processing. Further empirical research is needed to verify the BOI effects’ restrictive elements in the processing of English ergative verbs.     

Multiplicative Adjustment Method for Semiparametric Regression with Mixing Dependent Data

In this article, we extend a semiparametric regression estimator with multiplicative adjustment to time series context. The asymptotic theory and results from a simulation study are discussed. Theoretical results and numerical comparison show that, in the time series case, the semiparametric estimator is better than the traditional local polynomial estimator in a wide neighbourhood around the true regression function.     

Semi-Parametric Density Estimation for Time-Series with Multiplicative Adjustment

In this article, we extend a class of semi-parametric density estimators to time-series context. The asymptotic theory and simulation study are discussed. Theoretical results and numerical comparison show that in the time-series case, the estimators in this class are better than, or at least competitive with, the traditional kernel density estimator in a broad class of densities.     

Confidence Bounds for Multiplicative Comparisons

Statements that are inherently multiplicative have historically been justified using ratios of random variables. Although recent work on ratios has extended the classical theory to produce confidence bounds conditioned on a positive denominator, this current article offers a novel perspective that eliminates the need for such a condition. Although seemingly trivial, this new perspective leads to improved lower confidence bounds to support multiplicative statements. This perspective is also more satisfying as it allows comparisons that are inherently multiplicative in nature to be properly analyzed as such.     

Age-period-cohort analysis: an illustration of the problems in assessing interaction in one observation per cell data

This paper discusses the specific problems of age-period-cohort (A-P-C) analysis within the general framework of interaction assessment for two-way cross-classified data with one observation per cell. The A-P-C multiple classification model containing the effects of age groups (rows), periods of observation (columns), and birth cohorts (diagonals of the two-way table) is characterized as one of a special class of models involving interaction terms assumed to have very specific forms. The so-called A-P-C identification problem, which results from the use of a particular interaction structure for detecting cohort effects, is shown to manifest itself in the form of an exact linear dependency among the columns of the design matrix. The precise relationship holding among these columns is derived, as is an explicit formula for the bias in the parameter estimates resulting from an incorrect specification of an assumed restriction on the parameters required to solve the normal equations. Current methods for modeling A-P-C data are critically reviewed, an illustrative numerical example is presented, and one potentially promising analysis strategy is discussed. However, gien the large number of possible sources for error in A-P-C analyses, it is strongly recommended that the results of such analyses be interpreted with a great deal of caution.     

Nonparametric prediction analysis for binary data

A nonparametric inference algorithm developed by Davis and Geman (1983) is extended problem. The algorithm and applied to a medical prediction employs an estimation procedure for acquiring pairwise statistics among variables of a binary data set, allows for the data-driven creation of interaction terms among the variables, and employs a decision rule which asymptotically gives the minimum expected error. The inference procedure was designed for large data sets but has been extended via the method of cross-validation to encompass smaller data sets.     

BIVARIATE DISTRIBUTION WITH TRUNCATED POISSON MARGINAL DISTRIBUTIONS

ABSTRACT

A bivariate distribution, whose marginal distributions are truncated Poisson distributions, is developed as a product of truncated Poisson distributions and a multiplicative factor. The multiplicative factor takes into account the correlation, either positive or negative, between the two random variables. The distributional properties of this model are studied and the model is fitted to a real life bivariate data.     

Bayesian Forecasting With Stable Seasonal Patterns

A multiplicative seasonal forecasting model for cumulative events in which, conditional on end- of-season totals being given and seasonal shape being known, it is shown that events occurring within the season are multinomially distributed is presented. The model uses the information contained in the arrival of new events to obtain a posterior distribution for end-of-season totals. Bayesian forecasts are obtained recursively in two stages: first, by predicting the expected number and variance of event counts in future intervals within the remaining season, and then by predicting revised means and variances for end-of-season totals based on the most recent forecast error.     

A new approximation to the distribution function of the studentized maximum root

The Studentized maximum root (SMR) distribution is useful for constructing simultaneous confidence intervals around product interaction contrasts in replicated two-way ANOVA. A three-moment approximation to the SMR distribution is proposed. The approximation requires the first three moments of the maximum root of a central Wishart matrix. These values are obtained by means of numerical integration. The accuracy of the approximation is compared to the accuracy of a two-moment approximation for selected two-way table sizes. Both approximations are reasonably accurate. The three-moment approximation is generally superior.