论《怀念狼》三段论手法的审美意蕴

“三”是生活中常见的数字。民间叙事文学中“三”的运用形成了循环往复、意蕴丰富的艺术手法。《怀念狼》较为成功地将民间叙事的三段原理创化为审美三段论手法:人物塑造三段论在社会背景下塑造鲜活的人物形象,在人与自然的矛盾冲突中展示复杂的人物性格,挖掘人物悲剧性命运的美学意义;情节演进三段论通过单纯的意象性情节变化强化作品的可读性,探寻人物心路历程,展示广阔的社会生活和时代病症。     

Applications of a simple regression model to acid rain data

This paper addresses two vital and seemingly simple questions concerning wet deposition of sulfates, a major contributor to acid rain. The first is to estimate the annual wet deposition of sulfate at a monitoring site under emissions that existed during the 1980s. This problem is complicated by missing data. The second is to estimate accurately the effect of a change in pollution emissions on concentrations of sulfate in precipitation. This problem is made difficult by the substantial natural variations in sulfate concentrations due to weather variations between years. As a tool for addressing both of these problems, we develop a regression model for the sulfate concentration of precipitation on a particular day as a function of amount of precipitation and time of the year. The model is used as a basis for imputing missing sulfate concentrations to handle the missing-data problem. It is also used to explain some of the variability in annual sulfate concentrations due to meteorology and hence provide more accurate estimates of the effect of changes in emissions.     

Tukey's gh Distribution for Multiple Imputation

Tukey proposed a class of distributions, the g-and-h family (gh family), based on a transformation of a standard normal variable to accommodate different skewness and elongation in the distribution of variables arising in practical applications. It is easy to draw values from this distribution even though it is hard to explicitly state the probability density function. Given this flexibility, the gh family may be extremely useful in creating multiple imputations for missing data. This article demonstrates how this family, as well as its generalizations, can be used in the multiple imputation analysis of incomplete data. The focus of this article is on a scalar variable with missing values. In the absence of any additional information, data are missing completely at random, and hence the correct analysis is the complete-case analysis. Thus, the application of the gh multiple imputation to the scalar cases affords comparison with the correct analysis and with other model-based multiple imputation methods. Comparisons are made using simulated datasets and the data from a survey of adolescents ascertaining driving after drinking alcohol.     

Minimum breakdown designs in blocks of size two

In scientific investigations, there are many situations where each two experimental units have to be grouped into a block of size two. For planning such experiments, the variance-based optimality criteria like A-, D- and E-criterion are typically employed to choose efficient designs, if the estimation efficiency of treatment contrasts is primarily concerned. Alternatively, if there are observations which tend to become lost during the experimental period, the robustness criteria against the unavailability of data should be strongly recommended for selecting the planning scheme. In this study, a new criterion, called minimum breakdown criterion, is proposed to quantify the robustness of designs in blocks of size two. Based on the proposed criterion, a new class of robust designs, called minimum breakdown designs, is defined. When various numbers of blocks are missing, the minimum breakdown designs provide the highest probabilities that all the treatment contrasts are estimable. An exhaustive search procedure is proposed to generate such designs. In addition, two classes of uniformly minimum breakdown designs are theoretically verified.     

Statistical Matching Using File Concatenation With Adjusted Weights and Multiple Imputations

Statistically matched files are created in an attempt to solve the practical problem that exists when no single file has the full set of variables needed for drawing important inferences. Previous methods of file matching are reviewed, and the method of file concatenation with adjusted weights and multiple imputations is described and illustrated on an artificial example. A major benefit of this approach is the ability to display sensitivity of inference to untestable assumptions being made when creating the matched file.     

Decomposition of Prediction Error in Multilevel Models

ABSTRACT

We present a decomposition of prediction error for the multilevel model in the context of predicting a future observable y _*j in the jth group of a hierarchical dataset. The multilevel prediction rule is used for prediction and the components of prediction error are estimated via a simulation study that spans the various combinations of level-1 (individual) and level-2 (group) sample sizes and different intraclass correlation values. Additionally, analytical results present the increase in predicted mean square error (PMSE) with respect to prediction error bias. The components of prediction error provide information with respect to the cost of parameter estimation versus data imputation for predicting future values in a hierarchical data set. Specifically, the cost of parameter estimation is very small compared to data imputation.     

The Evaluation of the Performance of IPWGEE,a Simulation Study

This article compares two recently proposed test statistics for unobserved cluster effects (C, SSR _w ) with three statistics frequently mentioned in panel econometrics (BP, SLM, F). Simulations include data generating processes with a cluster-level explanatory variable, scenarios with unequally sized clusters, processes that have an incorrectly specified cluster structure, and processes that have no cluster structure but rather spatial correlation. All but the F test exhibit small-sample deviation from the asymptotic distribution. The SLM, F, and SSR _w tests show equivalent power when cluster sizes are balanced. SLM has greatest power when cluster sizes are unbalanced.     

Nonparametric Modeling Auxiliary Covariates in Random Coefficient Models

Random coefficient model (RCM) is a powerful statistical tool in analyzing correlated data collected from studies with different clusters or from longitudinal studies. In practice, there is a need for statistical methods that allow biomedical researchers to adjust for the measured and unmeasured covariates that might affect the regression model. This article studies two nonparametric methods dealing with auxiliary covariate data in linear random coefficient models. We demonstrate how to estimate the coefficients of the models and how to predict the random effects when the covariates are missing or mismeasured. We employ empirical estimator and kernel smoother to handle a discrete and continuous auxiliary, respectively. Simulation results show that the proposed methods perform better than an alternative method that only uses data in the validation data set and ignores the random effects in the random coefficient model.     

Fractional Regression Hot Deck Imputation Weight Adjustment

Fractional regression hot deck imputation (FRHDI) imputes multiple values for each instance of a missing dependent variable. The imputed values are equal to the predicted value plus multiple random residuals. Fractional weights enable variance estimation and preserve correlations. In some circumstances with some starting weight values, existing procedures for computing FRHDI weights can produce negative values. We discuss procedures for constructing non-negative adjusted fractional weights for FRHDI and study performance of the algorithm using simulation. The algorithm can be used effectively with FRDHI procedures for handling missing data in the context of a complex sample survey.     

Control chart for monitoring the Weibull shape parameter under two competing risks

ABSTRACT

In this paper, we propose a control chart to monitor the Weibull shape parameter where the observations are censored due to competing risks. We assume that the failure occurs due to two competing risks that are independent and follow Weibull distribution with different shape and scale parameters. The control charts are proposed to monitor one or both of the shape parameters of competing risk distributions and established based on the conditional expected values. The proposed control chart for both shape parameters is used in certain situations and allows to monitor both shape parameters in only one chart. The control limits depend on the sample size, number of failures due to each risk and the desired stable average run length (ARL). We also consider the estimation problem of the target parameters when the Phase I sample is incomplete. We assumed that some of the products that fail during the life testing have a cause of failure that is only known to belong to a certain subset of all possible failures. This case is known as masking. In the presence of masking, the expectation-maximization (EM) algorithm is proposed to estimate the parameters. For both cases, with and without masking, the behaviour of ARLs of charts is studied through the numerical methods. The influence of masking on the performance of proposed charts is also studied through a simulation study. An example illustrates the applicability of the proposed charts.