Package 'SIVMethod'

Unified simulation engine for Example B.2.1

Description

Unified simulation engine for Example B.2.1

Usage

Sim(n, no_sim, case, k = NULL)

Arguments

n	Integer. Number of observations.
no_sim	Integer. Number of simulation replications.
case	Character. One of '"A"', '"B"', '"C"' or '"D"'.
k	Integer. The truncation parameter.

Value

A list with:

output

Summary statistics matrix.

selection

Average selection frequencies.

Examples

Sim(100L, 200L, case = "A")

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Unified simulation engine for Example B.2.3

Description

This function combines Case A and Case B into a single package-ready simulation interface.

Usage

Sim_test(n, no_sim, case = "A", c = 0.75, k = NULL)

Arguments

n	Integer. Number of observations.
no_sim	Integer. Number of simulation replications.
case	Character. One of '"A"' or '"B"'.
c	Numeric scalar with c >= 0. Used in $\epsilon_i = c\sqrt{k/n}m(x_i)$. If c = 0, do not need to specify case.
k	Integer vector. The range of truncation parameters to be considered via Generalized Cross-Validation.

Value

A list with:

output

Summary statistics matrix.

TestRate_n

Average test rate.

Examples

Sim_test(100L, 200L, case = "A", c = 1)

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This function implements the SIV method for the linear form

Description

This function implements the SIV method for the linear form

Usage

SIV(Y, X, k)

Arguments

Y	Response vector, n-by-1.
X	Regressor matrix, n-by-d.
k	Integer vector including the possible values of the truncation parameter, L_k-by-1.

Value

A list containing the following components:

• b_SIV_*: Estimated coefficients (d-by-1).

• gam_SIV_*: Estimated gams.

• sd_*: Standard deviation of b_SIV_* and gam_SIV (d-by-1).

• e_*: Estimated residuals (n-by-1).

• eps_LSO: Estimated epsilon_i's of the paper (n-by-1), i.e., m(x_i) + e_i.

• V_LSO: The psi_i()'s after selection (n-by-k).

• Ind_LSO: Selection result for the LASSO method.

• m_all: Estimated m(.) and its 95% CI.

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