This presentation does require a narrative, and was designed for a 90 minute 'Lunch & Learn' environment. It showcases some of the opportunities that integrated digital marketing planning can address.
Corporate Event Management Company in Gurgaon Delhi NCRPrerana Saxena
Theme Weavers Designs is a leading corporate event planner and one of the top event management companies in Gurgaon Delhi NCR. We have significant experience in leadership offsites, corporate anniversaries and company and product launches as well as planning and organizing themed corporate events.
Missing-Value Handling in Dynamic Model Estimation using IMPL Alkis Vazacopoulos
Presented in this short document is a description of how IMPL handles missing-values or missing-data when estimating dynamic models which inherently involve time-lagged or time-shifted input and output variables. Missing-values in a data set imply that for some reason the data is not available most likely due to a mal-functioning instrument or even lack of proper accounting. Missing-data handling is relatively well-studied especially for time-series or dynamic data given that it is not as easy as removing, ignoring or deleting bad sections of data when static or steady-state models are calibrated (Honaker and King, 2010; Smits and Baggelaar, 2010; Fisher and Waclawski, 2015). Unfortunately, all of their methods involve what is known as “imputation” i.e., replacing or substituting missing-data with some reasonably assumed value which is at the very least is a biased estimate. When regression techniques such as PLS and PCR are used (Nelson et. al., 2006) then missing-data can be handled without imputation by computing the input-output covariance matrices excluding the contribution from the missing-values given the temporal and structural redundancy in the system. However, it is shown in Dayal (1996) that using PLS and other types of regression techniques such as Canonical Correlation Regression (CCR) and Reduced Rank Regression (RRR) to fit non-parsimonious and non-parametric finite impulse/step response models (FIR/FSR), that this is not as reliable as fitting lower-ordered transfer functions especially considering the robust stability of the resulting model predictive controller if that is its intended use.
Presentation to a Finance class at Illinois. Talks about how markets have changed, and how big data can integrate itself into tools for financial planning and trading-and to get a jump on the markets.
Benarkah Dukun Mengetahui Perkara Ghaib?Abu Muhammad
Benarkah Dukun Mengetahui Perkara Ghaib? Banyak kekeliruan dan kegelinciran akibat salah faham tentang dukun. Sebahagiannya melibatkan aqidah. Berhati hatilah kita semua
This presentation does require a narrative, and was designed for a 90 minute 'Lunch & Learn' environment. It showcases some of the opportunities that integrated digital marketing planning can address.
Corporate Event Management Company in Gurgaon Delhi NCRPrerana Saxena
Theme Weavers Designs is a leading corporate event planner and one of the top event management companies in Gurgaon Delhi NCR. We have significant experience in leadership offsites, corporate anniversaries and company and product launches as well as planning and organizing themed corporate events.
Missing-Value Handling in Dynamic Model Estimation using IMPL Alkis Vazacopoulos
Presented in this short document is a description of how IMPL handles missing-values or missing-data when estimating dynamic models which inherently involve time-lagged or time-shifted input and output variables. Missing-values in a data set imply that for some reason the data is not available most likely due to a mal-functioning instrument or even lack of proper accounting. Missing-data handling is relatively well-studied especially for time-series or dynamic data given that it is not as easy as removing, ignoring or deleting bad sections of data when static or steady-state models are calibrated (Honaker and King, 2010; Smits and Baggelaar, 2010; Fisher and Waclawski, 2015). Unfortunately, all of their methods involve what is known as “imputation” i.e., replacing or substituting missing-data with some reasonably assumed value which is at the very least is a biased estimate. When regression techniques such as PLS and PCR are used (Nelson et. al., 2006) then missing-data can be handled without imputation by computing the input-output covariance matrices excluding the contribution from the missing-values given the temporal and structural redundancy in the system. However, it is shown in Dayal (1996) that using PLS and other types of regression techniques such as Canonical Correlation Regression (CCR) and Reduced Rank Regression (RRR) to fit non-parsimonious and non-parametric finite impulse/step response models (FIR/FSR), that this is not as reliable as fitting lower-ordered transfer functions especially considering the robust stability of the resulting model predictive controller if that is its intended use.
Presentation to a Finance class at Illinois. Talks about how markets have changed, and how big data can integrate itself into tools for financial planning and trading-and to get a jump on the markets.
Benarkah Dukun Mengetahui Perkara Ghaib?Abu Muhammad
Benarkah Dukun Mengetahui Perkara Ghaib? Banyak kekeliruan dan kegelinciran akibat salah faham tentang dukun. Sebahagiannya melibatkan aqidah. Berhati hatilah kita semua