Balancing China's Seasonal Intercity Travel Demand: Alternatives for Freight Rail Expansion to Reduce Seasonal Passenger Rail Demand

Since 2010, China's annual domestic holiday travel for the 20-day season surrounding Spring Festival (a.k.a. Chinese New Year) has exceeded 200 million trips (China Transportation and Communication Yearbook). The demand surges have overwhelmedintercity transportation systems, particularly passenger rail. This transportation problem has emerged due to spatial economic imbalance: workers have had to travel betweentheir homes in rural hinterlands to factory jobs on the industrial coast, which had grown into amigratory population of 261,390,000 by 2010 (National Bureau of Statistics of China). The objectives of this research were: * to examine spatial relationships among factories, raw materials, markets, workers, and rail connections; and, * to identify how development of China's freight-rail industry can or will influence the Spring Festival travel season. Spatial analysis using geographic information systems (GIS), statistical hypothesis testing, and economic analysis including location quotients were conducted to examine spatial relationships among markets, factories, raw materials, workers, and rail connections. Potential was explored for developing freight rail to support inland vertical industry and employment that might reduce worker migration and thus reduce the surges of the Spring Festival travel season. It was concluded that Research results indicated sixteen inland provinces stood to develop vertical industries and integration. The inland provinces offered resources to support developing six main value-added industries: food processing, fiber development into cloth and textiles, wood and paper products from timber, tobacco products, metals, and machinery. Inland industrialization can offer employment to current migratory workers, thus reducing domestic passenger travel and the volume surges of Spring Festival seasonal demand. As movement of finished goods from hinterlands to the coast replaces movement of workers to coastal factory jobs, freight demand will increase. Increasing freight volumes across the country will produce pressure on the current freight railway network, leading to a need to reverse recent disinvestment by investing in freight infrastructure

Narrative Discourse Construction in Thomas Hardy’s Novels

The study is designed to concentrate on the narrative discourse construction in Thomas Hardy’s novels, based on close reading and textual analysis of the related works. Hardy ingeniously manipulates diverse discourse patterns merging the epistolary construction and dramatic presentation into his fiction. Epistolary construction enables Hardy to create intimacy between his characters and the readers, while dialogic structuring and dramatic monologue are applied to psychological description and analysis. It is concluded that both in time span and narrative discourse Hardy transcends the 19th century Victorian norms. In this sense, Hardy may be also acknowledged as a modernist writer for his proficiency in the manipulation of polyphony in discourse construction

Higher order influence functions and minimax estimation of nonlinear functionals

We present a theory of point and interval estimation for nonlinear functionals in parametric, semi-, and non-parametric models based on higher order influence functions (Robins (2004), Section 9; Li et al. (2004), Tchetgen et al. (2006), Robins et al. (2007)). Higher order influence functions are higher order U-statistics. Our theory extends the first order semiparametric theory of Bickel et al. (1993) and van der Vaart (1991) by incorporating the theory of higher order scores considered by Pfanzagl (1990), Small and McLeish (1994) and Lindsay and Waterman (1996). The theory reproduces many previous results, produces new non-$\sqrt{n}$ results, and opens up the ability to perform optimal non-$\sqrt{n}$ inference in complex high dimensional models. We present novel rate-optimal point and interval estimators for various functionals of central importance to biostatistics in settings in which estimation at the expected $\sqrt{n}$ rate is not possible, owing to the curse of dimensionality. We also show that our higher order influence functions have a multi-robustness property that extends the double robustness property of first order influence functions described by Robins and Rotnitzky (2001) and van der Laan and Robins (2003).Comment: Published in at http://dx.doi.org/10.1214/193940307000000527 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Inverse probability weighting for covariate adjustment in randomized studies

Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a 'favorable' model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two-stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a 'favorable' model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented

Asymptotic Normality of Quadratic Estimators

We prove conditional asymptotic normality of a class of quadratic U-statistics that are dominated by their degenerate second order part and have kernels that change with the number of observations. These statistics arise in the construction of estimators in high-dimensional semi- and non-parametric models, and in the construction of nonparametric confidence sets. This is illustrated by estimation of the integral of a square of a density or regression function, and estimation of the mean response with missing data. We show that estimators are asymptotically normal even in the case that the rate is slower than the square root of the observations