Factor-Driven Two-Regime Regression

We propose a novel two-regime regression model where regime switching is driven by a vector of possibly unobservable factors. When the factors are latent, we estimate them by the principal component analysis of a panel data set. We show that the optimization problem can be reformulated as mixed integer optimization, and we present two alternative computational algorithms. We derive the asymptotic distribution of the resulting estimator under the scheme that the threshold effect shrinks to zero. In particular, we establish a phase transition that describes the effect of first-stage factor estimation as the cross-sectional dimension of panel data increases relative to the time-series dimension. Moreover, we develop bootstrap inference and illustrate our methods via numerical studies

The Influence Of Knowledge Management On Market-Related Performance Through Business Process Effectiveness: An Empirical Investigation Of Hospitals And Financial Firms

Knowledge-based resources are critical in service sectors for facing the challenges of dynamic markets and helping organizations manage changes in consumer preference. Knowledge application is needed to improve the business process in order to attain superior market-related performance because there is the unperfected imitation coming from causal ambiguity. However, there is a lack of empirical study in examining the effect of KM and the effect of the business process within the scope of service sectors. This study examines how KM infrastructure supports and KM capabilities influence market-related performance through business processes effectiveness. Data collections of two studies are from 166 hospitals and 106 financial firms. The findings indicate a positive relationship between KM infrastructure and KM capability, and that they have a positive influence on market-related performance through business process effectiveness. For improving this process, the effect of KM infrastructure is greater than the effect of KM capabilities in hospitals. But the effect of KM capabilities is greater than the effect of KM infrastructure in financial firms. The implications of these findings for research and practices in hospitals and financial firms are also discussed

Sparse multidimensional exponential analysis with an application to radar imaging

We present a d-dimensional exponential analysis algorithm that offers a range of advantages compared to other methods. The technique does not suffer the curse of dimensionality and only needs O((d + 1)n) samples for the analysis of an n-sparse expression. It does not require a prior estimate of the sparsity n of the d-variate exponential sum. The method can work with sub-Nyquist sampled data and offers a validation step, which is very useful in low SNR conditions. A favourable computation cost results from the fact that d independent smaller systems are solved instead of one large system incorporating all measurements simultaneously. So the method also lends itself easily to a parallel execution. Our motivation to develop the technique comes from 2D and 3D radar imaging and is therefore illustrated on such examples

Fast Inference for Quantile Regression with Tens of Millions of Observations

Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial. Conventional econometric methods based on extreme estimators require large amounts of computing resources and memory, which are often not readily available. In this paper, we focus on linear quantile regression applied to ``ultra-large'' datasets, such as U.S. decennial censuses. A fast inference framework is presented, utilizing stochastic sub-gradient descent (S-subGD) updates. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing a pivotal statistic for inference using only a solution path. The methodology draws from time series regression to create an asymptotically pivotal statistic through random scaling. Our proposed test statistic is calculated in a fully online fashion and critical values are calculated without resampling. We conduct extensive numerical studies to showcase the computational merits of our proposed inference. For inference problems as large as $(n, d) \sim (10^7, 10^3)$, where $n$ is the sample size and $d$ is the number of regressors, our method generates new insights, surpassing current inference methods in computation. Our method specifically reveals trends in the gender gap in the U.S. college wage premium using millions of observations, while controlling over $10^3$ covariates to mitigate confounding effects.Comment: 45 pages, 6 figure

Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling

We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms. We leverage insights from time series regression in econometrics and construct asymptotically pivotal statistics via random scaling. Our approach is fully operational with online data and is rigorously underpinned by a functional central limit theorem. Our proposed inference method has a couple of key advantages over the existing methods. First, the test statistic is computed in an online fashion with only SGD iterates and the critical values can be obtained without any resampling methods, thereby allowing for efficient implementation suitable for massive online data. Second, there is no need to estimate the asymptotic variance and our inference method is shown to be robust to changes in the tuning parameters for SGD algorithms in simulation experiments with synthetic data.Comment: 16 pages, 5 figures, 5 table

New Orleans, Louisiana Paper Number: IMECE2002-MED-PPO-03 ADAPTIVE FEEDRATE SCHEDULING AND MATERIAL ENGAGEMENT ANALYSIS FOR HIGH PERFORMANCE MACHINING

ABSTRACT This paper presents a technique of feedrate scheduling by analyzing the material removal volume when a tool moves in linear, circular, or parametric curved motions. Tool motions of different types of endmilling cutters are considered in this study. By studying the relationship between the cutter geometry and the tool motion, the material removal rates of different cutters are analyzed. The adaptive feedrate scheduling can be determined to maintain a constant cutting load. The technique developed in this research can be used for tool path generation in CAD/CAM systems for 2.5D NC machining

HAPTIC SCULPTING AND 5-AXIS PENCIL-CUT PLANNING IN VIRTUAL PROTOTYPING AND MANUFACTURING

ABSTRACT In this paper, a Two-phase approach to tool collision detection and local gouging elimination is proposed for haptic pencil-cut of sculptured surfaces. Pencil-cut is a special kind of machining operation, whose purpose is to use relatively smaller tools to remove rest material on the corners or highly curved regions that are inaccessible by bigger tools. Tool orientation determination and tool collision avoidance are critical issues for 5-axis pencil-cut tool path planning. Detailed techniques of haptic rendering and tool interference avoidance are discussed for haptic-aided 5-axis pencil-cut tool path generation. Hardware and software implementation of the haptic pencil-cut system with practical examples are also presented in this paper