Inverse regression for longitudinal data

Sliced inverse regression (Duan and Li [Ann. Statist. 19 (1991) 505-530], Li [J. Amer. Statist. Assoc. 86 (1991) 316-342]) is an appealing dimension reduction method for regression models with multivariate covariates. It has been extended by Ferr\'{e} and Yao [Statistics 37 (2003) 475-488, Statist. Sinica 15 (2005) 665-683] and Hsing and Ren [Ann. Statist. 37 (2009) 726-755] to functional covariates where the whole trajectories of random functional covariates are completely observed. The focus of this paper is to develop sliced inverse regression for intermittently and sparsely measured longitudinal covariates. We develop asymptotic theory for the new procedure and show, under some regularity conditions, that the estimated directions attain the optimal rate of convergence. Simulation studies and data analysis are also provided to demonstrate the performance of our method.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1193 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org). With Correction

Digital Transformations in Taiwanese TV Industry

In the past, TV was always regarded as an indispensable member of every family. Watching TV programs with the whole family was once one of the key consumer behaviors. However, with the development of technology, the digital wave and the invasion of Over-The-Top (OTT)platforms, consumer behavior has begun to undergo drastic changes. Mobile phones and tablets occupy most of our time. Multi-screens have long become the norm. According to the Digital Whirlpool report published by IMD in 2019: Due to the impact of digital convergence, digital disruption has already occurred in the media, entertainment, and telecommunications industries. If digital transformation is not carried out in time, the next five may be replaced by other new services . Observe that the number of cable TV subscribers in Taiwan has dropped from 5.23 million in 2017. With the influence of online platforms and online pirated content, it has fallen all the way to the current low of 4.83 million in 2021.Facing the changes in viewers’ viewing behaviors and the shift in TV advertising budgets in recent years, various TV stations have also provided solutions and actively transformed from internal thinking to external environments. TV stations such as TVBS, Eastern Broadcasting Company (EBC), Sanli TV and Ctitv have begun their digital transformation

Cost-Sensitive Learning for Recurrence Prediction of Breast Cancer

Breast cancer is one of the top cancer-death causes and specifically accounts for 10.4% of all cancer incidences among women. The prediction of breast cancer recurrence has been a challenging research problem for many researchers. Data mining techniques have recently received considerable attention, especially when used for the construction of prognosis models from survival data. However, existing data mining techniques may not be effective to handle censored data. Censored instances are often discarded when applying classification techniques to prognosis. In this paper, we propose a cost-sensitive learning approach to involve the censored data in prognostic assessment with better recurrence prediction capability. The proposed approach employs an outcome inference mechanism to infer the possible probabilistic outcome of each censored instance and adopt the cost-proportionate rejection sampling and a committee machine strategy to take into account these instances with probabilistic outcomes during the classification model learning process. We empirically evaluate the effectiveness of our proposed approach for breast cancer recurrence prediction and include a censored-data-discarding method (i.e., building the recurrence prediction model by only using uncensored data) and the Kaplan-Meier method (a common prognosis method) as performance benchmarks. Overall, our evaluation results suggest that the proposed approach outperforms its benchmark techniques, measured by precision, recall and F1 score

Effect of temperature on the accumulation of marine biogenic gels in the surface microlayer near the outlet of nuclear power plants and adjacent areas in the Daya Bay, China

The surface microlayer (SML) in marine systems is often characterized by an enrichment of biogenic, gel-like particles, such as the polysaccharide-containing transparent exopolymer particles (TEP) and the protein-containing Coomassie stainable particles (CSP). This study investigated the distribution of TEP and CSP, in the SML and underlying water, as well as their bio-physical controlling factors in Daya Bay, an area impacted by warm discharge from two Nuclear power plants (Npp’s) and aquaculture during a research cruise in July 2014. The SML had higher proportions of cyanobacteria and of pico-size Chl a contrast to the underlayer water, particularly at the nearest outlet station characterized by higher temperature. Diatoms, dinoflagellates and chlorophyll a were depleted in the SML. Both CSP and TEP abundance and total area were enriched in the SML relative to the underlying water, with enrichment factors (EFs) of 1.5–3.4 for CSP numbers and 1.32–3.2 for TEP numbers. Although TEP and CSP showed highest concentration in the region where high productivity and high nutrient concertation were observed, EFs of gels and of dissolved organic carbon (DOC) and dissolved acidic polysaccharide (> 1 kDa), exhibited higher values near the outlet of the Npp’s than in the adjacent waters. The positive relation between EF’s of gels and temperature and the enrichment of cyanobacteria in the SML may be indicative of future conditions in a warmer ocean, suggesting potential effects on adjusting phytoplankton community, biogenic element cycling and air-sea exchange processe

Using the Taguchi Method and Finite Element Method to Analyze a Robust New Design for Titanium Alloy Prick Hole Extrusion

AbstractIn the process of prick hole extrusion, many factors must be controlled to obtain the required plastic strain and desired tolerance values. The major factors include lubricant, extrusion speed, billet temperature, and die angle. In this paper, we employed rigid-plastic finite element (FE) DEFORMTM software, to investigate the plastic deformation behavior of a titanium alloy (Ti-6Al-4V) billet as it was extruded through a conical prick hole die. We systematically examined the influence of the semi-cone angle on the prick hole die, the diameter of prick hole die, the factor of friction, the velocity of the ram and the temperature of the billet, under various extrusion conditions. We analyzed the strain, stress and damage factor distribution in the extrusion process. We used the Taguchi method to determine optimum design parameters, and our results confirmed the suitability of the proposed design, which enabled a prick hole die to achieve perfect extrusion during finite element testing