Nonparametric Kernel Methods with Errors‐in‐Variables: Constructing Estimators,Computing them,and Avoiding Common Mistakes

Estimating a curve nonparametrically from data measured with error is a difficult problem that has been studied by many authors. Constructing a consistent estimator in this context can sometimes be quite challenging, and in this paper we review some of the tools that have been developed in the literature for kernel‐based approaches, founded on the Fourier transform and a more general unbiased score technique. We use those tools to rederive some of the existing nonparametric density and regression estimators for data contaminated by classical or Berkson errors, and discuss how to compute these estimators in practice. We also review some mistakes made by those working in the area, and highlight a number of problems with an existing R package decon .     

Model‐based Prediction In Ecological Surveys Including Those with Incomplete Detection

This paper explores and develops model‐based predictors for surveys of plants and wildlife including those with incomplete detection. The methodology allows for estimating a detection function to account for objects which were not detected at the time of the survey. The model‐based theory utilises generalized linear models (GLMs) and is either new or adapted from other areas of sampling. A simulation study is used to validate the estimators and comparisons are made with an integrated likelihood approach. An aerial survey of kangaroos in western New South Wales is used to illustrate the theory. The area within 50m of the aircraft is treated as a strip transect and mark‐recapture methods are used to estimate the detection function.     

Restricted most powerful Bayesian tests for linear models

Uniformly most powerful Bayesian tests (UMPBTs) are a new class of Bayesian tests in which null hypotheses are rejected if their Bayes factor exceeds a specified threshold. The alternative hypotheses in UMPBTs are defined to maximize the probability that the null hypothesis is rejected. Here, we generalize the notion of UMPBTs by restricting the class of alternative hypotheses over which this maximization is performed, resulting in restricted most powerful Bayesian tests (RMPBTs). We then derive RMPBTs for linear models by restricting alternative hypotheses to g priors. For linear models, the rejection regions of RMPBTs coincide with those of usual frequentist F‐tests, provided that the evidence thresholds for the RMPBTs are appropriately matched to the size of the classical tests. This correspondence supplies default Bayes factors for many common tests of linear hypotheses. We illustrate the use of RMPBTs for ANOVA tests and t‐tests and compare their performance in numerical studies.     

大学校长该任多久 --对哈佛大学校长任期的考察及思考

通过对哈佛大学校长任期的考察,可以看出,首先要办成一所优秀的大学,校长任期制至关重要,其次,让年富力强,有开创精神的人走上大学校长的领导岗位,是大学校长长期任职的保证.     

Test for Trend With a Multinomial Outcome

ABSTRACT

There is no established procedure for testing for trend with nominal outcomes that would provide both a global hypothesis test and outcome-specific inference. We derive a simple formula for such a test using a weighted sum of Cochran–Armitage test statistics evaluating the trend in each outcome separately. The test is shown to be equivalent to the score test for multinomial logistic regression, however, the new formulation enables the derivation of a sample size formula and multiplicity-adjusted inference for individual outcomes. The proposed methods are implemented in the R package multiCA.     

Assessment of supplier performance based on product-development strategy by applying multi-granularity linguistic term sets

Measuring supplier performance possesses the properties of multi-dimension and multi-scale, it increases the complexity of the aggregation and assessment process. A focal company must develop an effective procedure for overcoming the complexity of performance measurement. Furthermore, the assessment of supplier performance must conform to the product-developed strategy of the focal company. This study is a further research deeper into the concept already introduced in Chang et al. [1] [Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle. International Journal of Production Economics 2006; 100(2):348–59]; using multi-granularity linguistic variable and numerical ration scale to represent the overall supply performance. By unifying the derived information, the measurement complexity could be resolved. The fuzzy preference was constructed to adjust the consistent direction and transform information into fuzzy relationship. Finally, fuzzy linguistic quantifier guided ordered weighted aggregation (FLQG-OWA) operator with maximal entropy was computed and aggregated with all indicators to meet the current policy of the focal company.     

All‐Pay Contests

This paper studies a class of games, “all‐pay contests,” which capture general asymmetries and sunk investments inherent in scenarios such as lobbying, competition for market power, labor‐market tournaments, and R&D races. Players compete for one of several identical prizes by choosing a score. Conditional on winning or losing, it is weakly better to do so with a lower score. This formulation allows for differing production technologies, costs of capital, prior investments, attitudes toward risk, and conditional and unconditional investments, among others. I provide a closed‐form formula for players' equilibrium payoffs and analyze player participation. A special case of contests is multiprize, complete‐information all‐pay auctions.     

Nonparametric Instrumental Regression

The focus of this paper is the nonparametric estimation of an instrumental regression function ϕ defined by conditional moment restrictions that stem from a structural econometric model E[Y−ϕ(Z)|W]=0, and involve endogenous variables Y and Z and instruments W. The function ϕ is the solution of an ill‐posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyzes identification and overidentification of this model, and presents asymptotic properties of the estimated nonparametric instrumental regression function.     

Real‐Time Delay Estimation Based on Delay History in Many‐Server Service Systems with Time‐Varying Arrivals

Motivated by interest in making delay announcements in service systems, we study real‐time delay estimators in many‐server service systems, both with and without customer abandonment. Our main contribution here is to consider the realistic feature of time‐varying arrival rates. We focus especially on delay estimators exploiting recent customer delay history. We show that time‐varying arrival rates can introduce significant estimation bias in delay‐history‐based delay estimators when the system experiences alternating periods of overload and underload. We then introduce refined delay‐history estimators that effectively cope with time‐varying arrival rates together with non‐exponential service‐time and abandonment‐time distributions, which are often observed in practice. We use computer simulation to verify that our proposed estimators outperform several natural alternatives.     

Pool‐Point Distribution of Zero‐Inventory Products

We study zero‐inventory production‐distribution systems under pool‐point delivery. The zero‐inventory production and distribution paradigm is supported in a variety of industries in which a product cannot be inventoried because of its short shelf life. The advantages of pool‐point (or hub‐and‐spoke) distribution, explored extensively in the literature, include the efficient use of transportation resources and effective day‐to‐day management of operations. The setting of our analysis is as follows: A production facility (plant) with a finite production rate distributes its single product, which cannot be inventoried, to several pool points. Each pool point may require multiple truckloads to satisfy its customers' demand. A third‐party logistics provider then transports the product to individual customers surrounding each pool point. The production rate can be increased up to a certain limit by incurring additional cost. The delivery of the product is done by identical trucks, each having limited capacity and non‐negligible traveling time between the plant and the pool points. Our objective is to coordinate the production and transportation operations so that the total cost of production and distribution is minimized, while respecting the product lifetime and the delivery capacity constraints. This study attempts to develop intuition into zero‐inventory production‐distribution systems under pool‐point delivery by considering several variants of the above setting. These include multiple trucks, a modifiable production rate, and alternative objectives. Using a combination of theoretical analysis and computational experiments, we gain insights into optimizing the total cost of a production‐delivery plan by understanding the trade‐off between production and transportation.