我国中小企业营销渠道困境与对策

随着经济的发展,我国中小企业日渐显著的作用引起社会的关注,但是中小企业在发展过程中一直存在着问题与困难,特别是遭遇到全球经济危机的波及,中小企业的营销对策陷入困境,其中,代表企业核心竞争力的渠道问题凸显。文章重在分析新时期中小企业营销渠道所面临的新的困境,并提出相应对策。     

Penalty and related estimation strategies in the spatial error model

Spatial autoregressive models are powerful tools in the analysis of data sets from diverse scientific areas of research such as econometrics, plant species richness, cancer mortality rates, image processing, analysis of the functional Magnetic Resonance Imaging (fMRI) data, and many more. An important class in the host of spatial autoregressive models is the class of spatial error models in which spatially lagged error terms are assumed. In this paper, we propose efficient shrinkage and penalty estimators for the regression coefficients of the spatial error model. We carry out asymptotic as well as simulation analyses to illustrate the gain in efficiency achieved by these new estimators. Furthermore, we apply the new methodology to housing prices data and provide a bootstrap approach to compute prediction errors of the new estimators.     

Realistic Statistical Modelling of Financial Data

The aim of this paper is to present some of the stylized features of financial data which have received a lot of attention both from practitioners and those with more theoretical backgrounds. Some of the models resulting from these efforts are reviewed and discussed. To facilitate the discussion two data sets are used: one of these contains all US trades in IBM stocks in 1995 at NYSE.     

Description of purchase incidence by multivariate heterogeneous Poisson processes

In this paper the application of bivariate Poisson heterogeneous models to budget data is studied. This study was motivated from inconsistencies that we encountered when univariate Poisson based models were applied to cumulative data sets. Application of a multivariate Poisson based model is a possible solution to this problem. In this paper we will study the feasibility of estimators based on these models.     

Modeling large data sets in marketing

In the last two decades, marketing databases have grown significantly in terms of size and richness of available information. The analysis of these databases raises several information-related and statistical issues. We aim at providing an overview of a selection of issues related to the analysis of large data sets. We focus on the two important areas: single source databases and customer transaction databases. We discuss models that have been used to describe customer behavior in these fields. Among the issues discussed are the development of parsimonious models, estimation methods, aggregation of data, data-fusion and the optimization of customer-level profit functions. We conclude that problems related to the analysis of large databases are far from resolved, and will stimulate new research avenues in the near future.     

Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models

In this paper we describe the use of modern numerical integration methods for making posterior inferences in composed error stochastic frontier models for panel data or individual cross- sections. Two Monte Carlo methods have been used in practical applications. We survey these two methods in some detail and argue that Gibbs sampling methods can greatly reduce the computational difficulties involved in analyzing such models.     

Confidence statements for efficiency estimates from stochastic frontier models

This paper is an empirical study of the uncertainty associated with technical efficiency estimates from stochastic frontier models. We show how to construct confidence intervals for estimates of technical efficiency levels under different sets of assumptions ranging from the very strong to the relatively weak. We demonstrate empirically how the degree of uncertainty associated with these estimates relates to the strength of the assumptions made and to various features of the data.     

Alternative interaction structures in square contingency tables having ordered classificatory variables

Goodman (1972) proposed several models for the analysis of the general I x I square tables with particular emphasis on social mobility data. We demonstrate in this paper, that most of his models can be reproduced by combinations of both new models proposed here and the various well known models that have received considerable attention in the literature. Our presentation here is both concise and simple to comprehend. The various models considered in this study are fitted to ten data sets that include the much analyzed 5×5 Danish and British Social mobility data sets. Results suggest that in some cases more parsimonious models than those considered earlier by various authors are possible for the explanations of the variations in the data analyzed in this study.     

Using the Fraction of Missing Information to Identify Auxiliary Variables for Imputation Procedures via Proxy Pattern‐mixture Models

In many surveys, imputation procedures are used to account for non‐response bias induced by either unit non‐response or item non‐response. Such procedures are optimised (in terms of reducing non‐response bias) when the models include covariates that are highly predictive of both response and outcome variables. To achieve this, we propose a method for selecting sets of covariates used in regression imputation models or to determine imputation cells for one or more outcome variables, using the fraction of missing information (FMI) as obtained via a proxy pattern‐mixture (PMM) model as the key metric. In our variable selection approach, we use the PPM model to obtain a maximum likelihood estimate of the FMI for separate sets of candidate imputation models and look for the point at which changes in the FMI level off and further auxiliary variables do not improve the imputation model. We illustrate our proposed approach using empirical data from the Ohio Medicaid Assessment Survey and from the Service Annual Survey.     

The Role of Statistics in the Data Revolution?

The nature of data is rapidly changing. Data sets are becoming increasingly large and complex. Modern methodology for analyzing these new types of data are emerging from the fields of Data Base Managment, Artificial Intelligence, Machine Learning, Pattern Recognition, and Data Visualization. So far Statistics as a field has played a minor role. This paper explores some of the reasons for this, and why statisticians should have an interest in participating in the development of new methods for large and complex data sets.     

Modelling Time Series of Counts in Epidemiology

We review generalized dynamic models for time series of count data. Usually temporal counts are modelled as following a Poisson distribution, and a transformation of the mean depends on parameters which evolve smoothly with time. We generalize the usual dynamic Poisson model by considering continuous mixtures of the Poisson distribution. We consider Poisson‐gamma and Poisson‐log‐normal mixture models. These models have a parameter for each time t which captures possible extra‐variation present in the data. If the time interval between observations is short, many observed zeros might result. We also propose zero inflated versions of the models mentioned above. In epidemiology, when a count is equal to zero, one does not know if the disease is present or not. Our model has a parameter which provides the probability of presence of the disease given no cases were observed. We rely on the Bayesian paradigm to obtain estimates of the parameters of interest, and discuss numerical methods to obtain samples from the resultant posterior distribution. We fit the proposed models to artificial data sets and also to a weekly time series of registered number of cases of dengue fever in a district of the city of Rio de Janeiro, Brazil, during 2001 and 2002.     

Regression Analysis under Probabilistic Multi‐Linkage

Linkage errors can occur when probability‐based methods are used to link records from two distinct data sets corresponding to the same target population. Current approaches to modifying standard methods of regression analysis to allow for these errors only deal with the case of two linked data sets and assume that the linkage process is complete, that is, all records on the two data sets are linked. This study extends these ideas to accommodate the situation when more than two data sets are probabilistically linked and the linkage is incomplete.     

我国中小企业营销渠道困境与对策

随着经济的发展,我国中小企业日渐显著的作用引起社会的关注,但是中小企业在发展过程中一直存在着问题与困难,特别是遭遇到全球经济危机的波及,中小企业的营销对策陷入困境,其中,代表企业核心竞争力的渠道问题凸显。文章重在分析新时期中小企业营销渠道所面临的新的困境,并提出相应对策。     

ERP远程接入实现技术应用案例研究

随着公司业务的发展,不同地区间分支机构的远程办公和移动办公需求激增,ERP软件遭遇远程接入应用需求的新难题。本文以浙江某投资集团公司为例,阐述了ERP远程接入Citrix解决方案的应用过程和实施效果,为企业利用基于服务器的计算技术解决ERP软件远程接入难题提供了经验。     

Estimating causal effects of credit decisions

In principle, making credit decisions under uncertainty can be approached by estimating the potential future outcomes that will result from the various decision alternatives. In practice, estimation difficulties may arise as a result of selection bias and limited historic testing. We review some theoretical results and practical estimation tools from observation study design and causal modeling, and evaluate their relevance to credit decision problems. Building on these results and tools, we propose a novel approach for estimating potential outcomes for credit decisions with multiple alternatives based on matching on multiple propensity scores. We demonstrate the approach and discuss results for risk-based pricing and credit line increase problems. Among the strengths of our approach are its transparency about data support for the estimates and its ability to incorporate prior knowledge in the extrapolative inference of treatment-response curves.     

Time-series–cross-section Data

This article treats the analysis of 'time-series–cross-section' (TSCS) data. Such data consists of repeated observations on a series of fixed units. Examples of such data are annual observations on the political economy of OECD nations in the post-war era. TSCS data is distinguished from 'panel' data, in that asymptotics are in the number of repeated observations, not the number of units.
The article begins by treating the complications of TSCS data in an 'old-fashioned' manner, that is, as a nuisance which causes estimation difficulties. It claims that TSCS data should be analyzed via ordinary least squares with 'panel correct standard errors' rather than generalized least squares methods. Dynamics should be modeled via a lagged dependent variable or, if appropriate, a single equation error correction model.
The article then treats more modern issues, in particular, the modeling of spatial effects and heterogeneity. It also claims that heterogeneity should be assessed with 'panel cross-validation' as well as more standard tests. The article concludes with a discussion of estimation in the presence of a binary dependent variable.     

Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting

This paper compares the forecast performance of automatic leading indicators (ALIs) and macroeconometric structural models (MESMs) commonly used by non-academic macroeconomists. Inflation and GDP growth form the forecast objects for comparison, using data from China, Indonesia and the Philippines. ALIs are found to outperform MESMs for one-period-ahead forecasts, but this superiority disappears as the forecast horizon increases. It is also found that ALIs involve greater uncertainty in choosing indicators, mixing data frequencies and utilizing unrestricted VARs. Two ways of reducing the uncertainty are explored: (i) give theory priority in choosing indicators, and include theory-based disequilibrium shocks in the indicator sets; and (ii) reduce the VARs by means of the general-to-specific modeling procedure.     

Forecasting sales response for multiple time horizons and temporally aggregated data: A comparison of constant and stochastic coefficient models

Past research on time-varying sales-response models emphasized the application of different estimation techniques in examining variation in advertising effectiveness over time. This study focuses on comparing sales forecasts using constant and stochastic coefficients sales-response models. Selected constant and stochastic coefficient models are applied to six sets of bimonthly and one set of annual advertising and sales data to assess forecasting accuracy for time horizons of various lengths. Results show improved forecasting accuracy for a first-order autoregressive stochastic coefficient model, particularly in short-run forecasting applications.     

The growth of the Internet,digital television and mobile telephony and the implications for not‐for‐profit marketing

This paper looks at what is currently known about who uses the new technologies and how they use them. It also sets out the current information available about the use of these technologies by the not‐for‐profit sector as a whole. The final section sets out the implications of these new technologies for not‐for‐profit marketers and the potential benefits for organisations that embrace and develop the new technologies. While the data presented focuses on the UK, the implications for not‐for‐profit marketing are relevant wherever the penetration in these technologies is high. Copyright © 2001 Henry Stewart Publications     

Methods for inference in large multiple-equation Markov-switching models

Inference for multiple-equation Markov-chain models raises a number of difficulties that are unlikely to appear in smaller models. Our framework allows for many regimes in the transition matrix, without letting the number of free parameters grow as the square as the number of regimes, but also without losing a convenient form for the posterior distribution. Calculation of marginal data densities is difficult in these high-dimensional models. This paper gives methods to overcome these difficulties, and explains why existing methods are unreliable. It makes suggestions for maximizing posterior density and initiating MCMC simulations that provide robustness against the complex likelihood shape.